qualitative and quantitative data analysis in research

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Educational resources and simple solutions for your research journey

Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

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Quantitative vs Qualitative Data: What’s the Difference?

If you’re considering a career in data—or in any kind of research field, like psychology—you’ll need to get to grips with two types of data: Quantitative and qualitative .

Quantitative data is anything that can be counted or measured ; it refers to numerical data. Qualitative data is descriptive , referring to things that can be observed but not measured—such as colors or emotions.

In this post, we’ll define both quantitative and qualitative data in more detail. We’ll then explore all the key ways in which they differ—from how they are collected and analyzed, to the advantages and disadvantages of each. We’ll also include useful examples throughout.

By the end, you’ll have a clear understanding of the difference between qualitative and quantitative data, and a good idea of when to use which. Want to skip ahead to a specific section? Just use this clickable menu:

• Quantitative vs qualitative data: What are they, and what’s the difference between them?
• What are the different types of quantitative and qualitative data?
• How are quantitative and qualitative data collected?
• Quantitative vs qualitative data: Methods of analysis
• What are the advantages and disadvantages of quantitative vs qualitative data?
• When should I use qualitative or quantitative data?
• Quantitative vs. qualitative data: FAQ
• Key takeaways 

Without further ado, let’s jump in.

1. What is the difference between quantitative and qualitative data?

When it comes to conducting research and data analysis, you’ll work with two types of data: quantitative and qualitative. Each requires different collection and analysis methods, so it’s important to understand the difference between the two.

What is quantitative data?

Quantitative data refers to any information that can be quantified. If it can be counted or measured, and given a numerical value, it’s quantitative data. Quantitative data can tell you “how many,” “how much,” or “how often”—for example, how many people attended last week’s webinar? How much revenue did the company make in 2019? How often does a certain customer group use online banking?

To analyze and make sense of quantitative data, you’ll conduct statistical analyses.

Learn more: What is quantitative data? A complete introduction

What is qualitative data?

Unlike quantitative data, qualitative data cannot be measured or counted. It’s descriptive, expressed in terms of language rather than numerical values.

Researchers will often turn to qualitative data to answer “Why?” or “How?” questions. For example, if your quantitative data tells you that a certain website visitor abandoned their shopping cart three times in one week, you’d probably want to investigate why—and this might involve collecting some form of qualitative data from the user. Perhaps you want to know how a user feels about a particular product; again, qualitative data can provide such insights. In this case, you’re not just looking at numbers; you’re asking the user to tell you, using language, why they did something or how they feel.

Qualitative data also refers to the words or labels used to describe certain characteristics or traits—for example, describing the sky as blue or labeling a particular ice cream flavor as vanilla.

What are the main differences between quantitative and qualitative data?

The main differences between quantitative and qualitative data lie in what they tell us , how they are collected , and how they are analyzed. Let’s summarize the key differences before exploring each aspect in more detail:

• Quantitative data is countable or measurable, relating to numbers. Qualitative data is descriptive, relating to language.
• Quantitative data tells us how many, how much, or how often (e.g. “20 people signed up to our email newsletter last week”). Qualitative data can help us to understand the “why” or “how” behind certain behaviors, or it can simply describe a certain attribute—for example, “The postbox is red” or “I signed up to the email newsletter because I’m really interested in hearing about local events.”
• Quantitative data is fixed and “universal,” while qualitative data is subjective and dynamic. For example, if something weighs 20 kilograms, that can be considered an objective fact. However, two people may have very different qualitative accounts of how they experience a particular event.
• Quantitative data is gathered by measuring and counting. Qualitative data is collected by interviewing and observing.
• Quantitative data is analyzed using statistical analysis, while qualitative data is analyzed by grouping it in terms of meaningful categories or themes.

The difference between quantitative and qualitative data: An example

To illustrate the difference between quantitative and qualitative data, let’s use an example. Imagine you want to describe your best friend. What kind of data might you gather or use to paint a vivid picture?

First, you might describe their physical attributes, such as their height, their hair style and color, what size feet they have, and how much they weigh. Then you might describe some of their most prominent personality traits. On top of that, you could describe how many siblings and pets they have, where they live, and how often they go swimming (their favorite hobby).

All of that data will fall into either the quantitative or qualitative categories, as follows:

Quantitative data:

• My best friend is 5 feet and 7 inches tall
• They have size 6 feet
• They weigh 63 kilograms
• My best friend has one older sibling and two younger siblings
• They have two cats
• My best friend lives twenty miles away from me
• They go swimming four times a week

Qualitative data:

• My best friend has curly brown hair
• They have green eyes
• My best friend is funny, loud, and a good listener
• They can also be quite impatient and impulsive at times
• My best friend drives a red car
• They have a very friendly face and a contagious laugh

Of course, when working as a researcher or data analyst, you’ll be handling much more complex data than the examples we’ve given. However, our “best friend” example has hopefully made it easier for you to distinguish between quantitative and qualitative data.

2. Different types of quantitative and qualitative data

When considering the difference between quantitative and qualitative data, it helps to explore some types and examples of each. Let’s do that now, starting with quantitative data.

Types of quantitative data (with examples)

Quantitative data is either discrete or continuous :

• Discrete quantitative data takes on fixed numerical values and cannot be broken down further. An example of discrete data is when you count something, such as the number of people in a room. If you count 32 people, this is fixed and finite.
• Continuous quantitative data can be placed on a continuum and infinitely broken down into smaller units. It can take any value; for example, a piece of string can be 20.4cm in length, or the room temperature can be 30.8 degrees.

What are some real-world examples of quantitative data?

Some everyday examples of quantitative data include:

• Measurements such as height, length, and weight
• Counts, such as the number of website visitors, sales, or email sign-ups
• Calculations, such as revenue
• Projections, such as predicted sales or projected revenue increase expressed as a percentage
• Quantification of qualitative data—for example, asking customers to rate their satisfaction on a scale of 1-5 and then coming up with an overall customer satisfaction score

Types of qualitative data (with examples)

Qualitative data may be classified as nominal or ordinal :

• Nominal data is used to label or categorize certain variables without giving them any type of quantitative value. For example, if you were collecting data about your target audience, you might want to know where they live. Are they based in the UK, the USA, Asia, or Australia? Each of these geographical classifications count as nominal data. Another simple example could be the use of labels like “blue,” “brown,” and “green” to describe eye color.
• Ordinal data is when the categories used to classify your qualitative data fall into a natural order or hierarchy. For example, if you wanted to explore customer satisfaction, you might ask each customer to select whether their experience with your product was “poor,” “satisfactory,” “good,” or “outstanding.” It’s clear that “outstanding” is better than “poor,” but there’s no way of measuring or quantifying the “distance” between the two categories.

Nominal and ordinal data tends to come up within the context of conducting questionnaires and surveys. However, qualitative data is not just limited to labels and categories; it also includes unstructured data such as what people say in an interview, what they write in a product review, or what they post on social media.

What are some real-world examples of qualitative data?

Some examples of qualitative data include:

• Interview transcripts or audio recordings
• The text included in an email or social media post
• Product reviews and customer testimonials
• Observations and descriptions; e.g. “I noticed that the teacher was wearing a red jumper.”
• Labels and categories used in surveys and questionnaires, e.g. selecting whether you are satisfied, dissatisfied, or indifferent to a particular product or service.

3. How are quantitative and qualitative data collected?

One of the key differences between quantitative and qualitative data is in how they are collected or generated.

How is quantitative data generated?

Quantitative data is generated by measuring or counting certain entities, or by performing calculations. Some common quantitative data collection methods include:

• Surveys and questionnaires: This is an especially useful method for gathering large quantities of data. If you wanted to gather quantitative data on employee satisfaction, you might send out a survey asking them to rate various aspects of the organization on a scale of 1-10.
• Analytics tools: Data analysts and data scientists use specialist tools to gather quantitative data from various sources. For example, Google Analytics gathers data in real-time, allowing you to see, at a glance, all the most important metrics for your website—such as traffic, number of page views, and average session length.
• Environmental sensors: A sensor is a device which detects changes in the surrounding environment and sends this information to another electronic device, usually a computer. This information is converted into numbers, providing a continuous stream of quantitative data.
• Manipulation of pre-existing quantitative data: Researchers and analysts will also generate new quantitative data by performing statistical analyses or calculations on existing data. For example, if you have a spreadsheet containing data on the number of sales and expenditures in USD, you could generate new quantitative data by calculating the overall profit margin.

How is qualitative data generated?

Qualitative data is gathered through interviews, surveys, and observations. Let’s take a look at these methods in more detail:

• Interviews are a great way to learn how people feel about any given topic—be it their opinions on a new product or their experience using a particular service. Conducting interviews will eventually provide you with interview transcripts which can then be analyzed.
• Surveys and questionnaires are also used to gather qualitative data. If you wanted to collect demographic data about your target audience, you might ask them to complete a survey where they either select their answers from a number of different options, or write their responses as freeform text.
• Observations: You don’t necessarily have to actively engage with people in order to gather qualitative data. Analysts will also look at “naturally occurring” qualitative data, such as the feedback left in product reviews or what people say in their social media posts.

4. Quantitative vs qualitative data: methods of analysis

Another major difference between quantitative and qualitative data lies in how they are analyzed. Quantitative data is suitable for statistical analysis and mathematical calculations, while qualitative data is usually analyzed by grouping it into meaningful categories or themes.

Quantitative data analysis

How you analyze your quantitative data depends on the kind of data you’ve gathered and the insights you want to uncover. Statistical analysis can be used to identify trends in the data, to establish if there’s any kind of relationship between a set of variables (e.g. does social media spend correlate with sales), to calculate probability in order to accurately predict future outcomes, to understand how the data is distributed—and much, much more.

Some of the most popular methods used by data analysts include:

• Regression analysis
• Monte Carlo simulation
• Factor analysis
• Cohort analysis
• Cluster analysis
• Time series analysis

You’ll find a detailed explanation of these methods in our guide to the most useful data analysis techniques .

Qualitative data analysis

With qualitative data analysis, the focus is on making sense of unstructured data (such as large bodies of text). Given that qualitative data cannot be measured objectively, it is open to subjective interpretation and therefore requires a different approach to analysis.

The main method of analysis used with qualitative data is a technique known as thematic analysis. Essentially, the data is coded in order to identify recurring keywords or topics, and then, based on these codes, grouped into meaningful themes.

Another type of analysis is sentiment analysis , which seeks to classify and interpret the emotions conveyed within textual data. This allows businesses to gauge how customers feel about various aspects of the brand, product, or service, and how common these sentiments are across the entire customer base.

Traditionally, qualitative data analysis has had something of a bad reputation for being extremely time-consuming. However, nowadays the process can be largely automated, and there are plenty of tools and software out there to help you make sense of your qualitative data. To learn more about qualitative analysis and what you can do with it, check out this round-up of the most useful qualitative analysis tools on the market .

5. What are the advantages and disadvantages of quantitative vs qualitative data?

Each type of data comes with advantages and disadvantages, and it’s important to bear these in mind when conducting any kind of research or sourcing data for analysis. We’ll outline the main advantages and disadvantages of each now.

What are the advantages and disadvantages of quantitative data?

A big advantage of quantitative data is that it’s relatively quick and easy to collect, meaning you can work with large samples. At the same time, quantitative data is objective; it’s less susceptible to bias than qualitative data, which makes it easier to draw reliable and generalizable conclusions.

The main disadvantage of quantitative data is that it can lack depth and context. The numbers don’t always tell you the full story; for example, you might see that you lost 70% of your newsletter subscribers in one week, but without further investigation, you won’t know why.

What are the advantages and disadvantages of qualitative data?

Where quantitative data falls short, qualitative data shines. The biggest advantage of qualitative data is that it offers rich, in-depth insights and allows you to explore the context surrounding a given topic. Through qualitative data, you can really gauge how people feel and why they take certain actions—crucial if you’re running any kind of organization and want to understand how your target audience operates.

However, qualitative data can be harder and more time-consuming to collect, so you may find yourself working with smaller samples. Because of its subjective nature, qualitative data is also open to interpretation, so it’s important to be aware of bias when conducting qualitative analysis.

6. When should I use qualitative or quantitative data?

Put simply, whether you use qualitative or quantitative data (or a combination of both!) depends on the data analytics project you’re undertaking. Here, we’ll discuss which projects are better suited to which data.

Generally, you can use the following criteria to determine whether to go with qualitative data, quantitative data, or a mixed methods approach to collecting data for your project.

• Do you want to understand something, such as a concept, experience, or opinions? Use qualitative data.
• Do you want to confirm or test something, such as a theory or hypothesis? Use quantitative data.
• Are you taking on research? You may benefit from a mixed methods approach to data collection.

You may find that more often than not, both types of data are used in projects, in order to gain a clear overall image—integrating both the numbers side and human side of things.

6. Quantitative vs. qualitative data: FAQ

What are the main differences between qualitative and quantitative research.

Qualitative research is primarily exploratory and uses non-numerical data to understand underlying reasons, opinions, and motivations. Quantitative research, on the other hand, is numerical and seeks to measure variables and relationships through statistical analysis. Additionally, qualitative research tends to be subjective and less structured, while quantitative research is objective and more structured.

What are examples of qualitative and quantitative data?

Examples of qualitative data include open-ended survey responses, interview transcripts, and observational notes. Examples of quantitative data include numerical survey responses, test scores, and website traffic data. Qualitative data is typically subjective and descriptive, while quantitative data is objective and numerical.

7. Key takeaways

Throughout this post, we’ve defined quantitative and qualitative data and explained how they differ. What it really boils down to, in very simple terms, is that quantitative data is countable or measurable, relating to numbers, while qualitative data is descriptive, relating to language.

Understanding the difference between quantitative and qualitative data is one of the very first steps towards becoming a data expert. If you’re considering a career in data, you’ll find links to some useful articles at the end of this post. Had enough theory and want some action? Check out our list of free data analytics courses for beginners , or cut to the chase and simply sign up for a free, five-day introductory data analytics short course .

• A step-by-step guide to the data analysis process
• What is the typical data analyst career path?
• The best data analytics courses in 2022

Qualitative vs Quantitative Research 101

A plain-language explanation (with examples).

By: Kerryn Warren (PhD, MSc, BSc) | June 2020

So, it’s time to decide what type of research approach you’re going to use – qualitative or quantitative . And, chances are, you want to choose the one that fills you with the least amount of dread. The engineers may be keen on quantitative methods because they loathe interacting with human beings and dealing with the “soft” stuff and are far more comfortable with numbers and algorithms. On the other side, the anthropologists are probably more keen on qualitative methods because they literally have the opposite fears.

However, when justifying your research, “being afraid” is not a good basis for decision making. Your methodology needs to be informed by your research aims and objectives , not your comfort zone. Plus, it’s quite common that the approach you feared (whether qualitative or quantitative) is actually not that big a deal. Research methods can be learnt (usually a lot faster than you think) and software reduces a lot of the complexity of both quantitative and qualitative data analysis. Conversely, choosing the wrong approach and trying to fit a square peg into a round hole is going to create a lot more pain.

In this post, I’ll explain the qualitative vs quantitative choice in straightforward, plain language with loads of examples. This won’t make you an expert in either, but it should give you a good enough “big picture” understanding so that you can make the right methodological decision for your research.

Qualitative vs Quantitative: Overview  

• Qualitative analysis 101
• Quantitative analysis 101
• How to choose which one to use
• Data collection and analysis for qualitative and quantitative research
• The pros and cons of both qualitative and quantitative research
• A quick word on mixed methods

Qualitative Research 101: The Basics

The bathwater is hot.

Let us unpack that a bit. What does that sentence mean? And is it useful?

The answer is: well, it depends. If you’re wanting to know the exact temperature of the bath, then you’re out of luck. But, if you’re wanting to know how someone perceives the temperature of the bathwater, then that sentence can tell you quite a bit if you wear your qualitative hat .

Many a husband and wife have never enjoyed a bath together because of their strongly held, relationship-destroying perceptions of water temperature (or, so I’m told). And while divorce rates due to differences in water-temperature perception would belong more comfortably in “quantitative research”, analyses of the inevitable arguments and disagreements around water temperature belong snugly in the domain of “qualitative research”. This is because qualitative research helps you understand people’s perceptions and experiences  by systematically coding and analysing the data .

With qualitative research, those heated disagreements (excuse the pun) may be analysed in several ways. From interviews to focus groups to direct observation (ideally outside the bathroom, of course). You, as the researcher, could be interested in how the disagreement unfolds, or the emotive language used in the exchange. You might not even be interested in the words at all, but in the body language of someone who has been forced one too many times into (what they believe) was scalding hot water during what should have been a romantic evening. All of these “softer” aspects can be better understood with qualitative research.

In this way, qualitative research can be incredibly rich and detailed , and is often used as a basis to formulate theories and identify patterns. In other words, it’s great for exploratory research (for example, where your objective is to explore what people think or feel), as opposed to confirmatory research (for example, where your objective is to test a hypothesis). Qualitative research is used to understand human perception , world view and the way we describe our experiences. It’s about exploring and understanding a broad question, often with very few preconceived ideas as to what we may find.

But that’s not the only way to analyse bathwater, of course…

Quantitative Research 101: The Basics

The bathwater is 45 degrees Celsius.

Now, what does this mean? How can this be used?

I was once told by someone to whom I am definitely not married that he takes regular cold showers. As a person who is terrified of anything that isn’t body temperature or above, this seemed outright ludicrous. But this raises a question: what is the perfect temperature for a bath? Or at least, what is the temperature of people’s baths more broadly? (Assuming, of course, that they are bathing in water that is ideal to them). To answer this question, you need to now put on your quantitative hat .

If we were to ask 100 people to measure the temperature of their bathwater over the course of a week, we could get the average temperature for each person. Say, for instance, that Jane averages at around 46.3°C. And Billy averages around 42°C. A couple of people may like the unnatural chill of 30°C on the average weekday. And there will be a few of those striving for the 48°C that is apparently the legal limit in England (now, there’s a useless fact for you).

With a quantitative approach, this data can be analysed in heaps of ways. We could, for example, analyse these numbers to find the average temperature, or look to see how much these temperatures vary. We could see if there are significant differences in ideal water temperature between the sexes, or if there is some relationship between ideal bath water temperature and age! We could pop this information onto colourful, vibrant graphs , and use fancy words like “significant”, “correlation” and “eigenvalues”. The opportunities for nerding out are endless…

In this way, quantitative research often involves coming into your research with some level of understanding or expectation regarding the outcome, usually in the form of a hypothesis that you want to test. For example:

Hypothesis: Men prefer bathing in lower temperature water than women do.

This hypothesis can then be tested using statistical analysis. The data may suggest that the hypothesis is sound, or it may reveal that there are some nuances regarding people’s preferences. For example, men may enjoy a hotter bath on certain days.

So, as you can see, qualitative and quantitative research each have their own purpose and function. They are, quite simply, different tools for different jobs .

Need a helping hand?

Qualitative vs Quantitative Research: Which one should you use?

And here I become annoyingly vague again. The answer: it depends. As I alluded to earlier, your choice of research approach depends on what you’re trying to achieve with your research. 

If you want to understand a situation with richness and depth , and you don’t have firm expectations regarding what you might find, you’ll likely adopt a qualitative research approach. In other words, if you’re starting on a clean slate and trying to build up a theory (which might later be tested), qualitative research probably makes sense for you.

On the other hand, if you need to test an already-theorised hypothesis , or want to measure and describe something numerically, a quantitative approach will probably be best. For example, you may want to quantitatively test a theory (or even just a hypothesis) that was developed using qualitative research.

Basically, this means that your research approach should be chosen based on your broader research aims , objectives and research questions . If your research is exploratory and you’re unsure what findings may emerge, qualitative research allows you to have open-ended questions and lets people and subjects speak, in some ways, for themselves. Quantitative questions, on the other hand, will not. They’ll often be pre-categorised, or allow you to insert a numeric response. Anything that requires measurement , using a scale, machine or… a thermometer… is going to need a quantitative method.

Let’s look at an example.

Say you want to ask people about their bath water temperature preferences. There are many ways you can do this, using a survey or a questionnaire – here are 3 potential options:

• How do you feel about your spouse’s bath water temperature preference? (Qualitative. This open-ended question leaves a lot of space so that the respondent can rant in an adequate manner).
• What is your preferred bath water temperature? (This one’s tricky because most people don’t know or won’t have a thermometer, but this is a quantitative question with a directly numerical answer).
• Most people who have commented on your bath water temperature have said the following (choose most relevant): It’s too hot. It’s just right. It’s too cold. (Quantitative, because you can add up the number of people who responded in each way and compare them).

The answers provided can be used in a myriad of ways, but, while quantitative responses are easily summarised through counting or calculations, categorised and visualised, qualitative responses need a lot of thought and are re-packaged in a way that tries not to lose too much meaning.

Qualitative vs Quantitative Research: Data collection and analysis

The approach to collecting and analysing data differs quite a bit between qualitative and quantitative research.

A qualitative research approach often has a small sample size (i.e. a small number of people researched) since each respondent will provide you with pages and pages of information in the form of interview answers or observations. In our water perception analysis, it would be super tedious to watch the arguments of 50 couples unfold in front of us! But 6-10 would be manageable and would likely provide us with interesting insight into the great bathwater debate.

To sum it up, data collection in qualitative research involves relatively small sample sizes but rich and detailed data.

On the other side, quantitative research relies heavily on the ability to gather data from a large sample and use it to explain a far larger population (this is called “generalisability”). In our bathwater analysis, we would need data from hundreds of people for us to be able to make a universal statement (i.e. to generalise), and at least a few dozen to be able to identify a potential pattern. In terms of data collection, we’d probably use a more scalable tool such as an online survey to gather comparatively basic data.

So, compared to qualitative research, data collection for quantitative research involves large sample sizes but relatively basic data.

Both research approaches use analyses that allow you to explain, describe and compare the things that you are interested in. While qualitative research does this through an analysis of words, texts and explanations, quantitative research does this through reducing your data into numerical form or into graphs.

There are dozens of potential analyses which each uses. For example, qualitative analysis might look at the narration (the lamenting story of love lost through irreconcilable water toleration differences), or the content directly (the words of blame, heat and irritation used in an interview). Quantitative analysis  may involve simple calculations for averages , or it might involve more sophisticated analysis that assesses the relationships between two or more variables (for example, personality type and likelihood to commit a hot water-induced crime). We discuss the many analysis options other blog posts, so I won’t bore you with the details here.

Qualitative vs Quantitative Research: The pros & cons on both sides

Quantitative and qualitative research fundamentally ask different kinds of questions and often have different broader research intentions. As I said earlier, they are different tools for different jobs – so we can’t really pit them off against each other. Regardless, they still each have their pros and cons.

Let’s start with qualitative “pros”

Qualitative research allows for richer , more insightful (and sometimes unexpected) results. This is often what’s needed when we want to dive deeper into a research question . When we want to find out what and how people are thinking and feeling , qualitative is the tool for the job. It’s also important research when it comes to discovery and exploration when you don’t quite know what you are looking for. Qualitative research adds meat to our understanding of the world and is what you’ll use when trying to develop theories.

Qualitative research can be used to explain previously observed phenomena , providing insights that are outside of the bounds of quantitative research, and explaining what is being or has been previously observed. For example, interviewing someone on their cold-bath-induced rage can help flesh out some of the finer (and often lost) details of a research area. We might, for example, learn that some respondents link their bath time experience to childhood memories where hot water was an out of reach luxury. This is something that would never get picked up using a quantitative approach.

There are also a bunch of practical pros to qualitative research. A small sample size means that the researcher can be more selective about who they are approaching. Linked to this is affordability . Unless you have to fork out huge expenses to observe the hunting strategies of the Hadza in Tanzania, then qualitative research often requires less sophisticated and expensive equipment for data collection and analysis.

Qualitative research also has its “cons”:

A small sample size means that the observations made might not be more broadly applicable. This makes it difficult to repeat a study and get similar results. For instance, what if the people you initially interviewed just happened to be those who are especially passionate about bathwater. What if one of your eight interviews was with someone so enraged by a previous experience of being run a cold bath that she dedicated an entire blog post to using this obscure and ridiculous example?

But sample is only one caveat to this research. A researcher’s bias in analysing the data can have a profound effect on the interpretation of said data. In this way, the researcher themselves can limit their own research. For instance, what if they didn’t think to ask a very important or cornerstone question because of previously held prejudices against the person they are interviewing?

Adding to this, researcher inexperience is an additional limitation . Interviewing and observing are skills honed in over time. If the qualitative researcher is not aware of their own biases and limitations, both in the data collection and analysis phase, this could make their research very difficult to replicate, and the theories or frameworks they use highly problematic.

Qualitative research takes a long time to collect and analyse data from a single source. This is often one of the reasons sample sizes are pretty small. That one hour interview? You are probably going to need to listen to it a half a dozen times. And read the recorded transcript of it a half a dozen more. Then take bits and pieces of the interview and reformulate and categorize it, along with the rest of the interviews.

Now let’s turn to quantitative “pros”:

Even simple quantitative techniques can visually and descriptively support or reject assumptions or hypotheses . Want to know the percentage of women who are tired of cold water baths? Boom! Here is the percentage, and a pie chart. And the pie chart is a picture of a real pie in order to placate the hungry, angry mob of cold-water haters.

Quantitative research is respected as being objective and viable . This is useful for supporting or enforcing public opinion and national policy. And if the analytical route doesn’t work, the remainder of the pie can be thrown at politicians who try to enforce maximum bath water temperature standards. Clear, simple, and universally acknowledged. Adding to this, large sample sizes, calculations of significance and half-eaten pies, don’t only tell you WHAT is happening in your data, but the likelihood that what you are seeing is real and repeatable in future research. This is an important cornerstone of the scientific method.

Quantitative research can be pretty fast . The method of data collection is faster on average: for instance, a quantitative survey is far quicker for the subject than a qualitative interview. The method of data analysis is also faster on average. In fact, if you are really fancy, you can code and automate your analyses as your data comes in! This means that you don’t necessarily have to worry about including a long analysis period into your research time.

Lastly – sometimes, not always, quantitative research may ensure a greater level of anonymity , which is an important ethical consideration . A survey may seem less personally invasive than an interview, for instance, and this could potentially also lead to greater honesty. Of course, this isn’t always the case. Without a sufficient sample size, respondents can still worry about anonymity – for example, a survey within a small department.

But there are also quantitative “cons”:

Quantitative research can be comparatively reductive – in other words, it can lead to an oversimplification of a situation. Because quantitative analysis often focuses on the averages and the general relationships between variables, it tends to ignore the outliers. Why is that one person having an ice bath once a week? With quantitative research, you might never know…

It requires large sample sizes to be used meaningfully. In order to claim that your data and results are meaningful regarding the population you are studying, you need to have a pretty chunky dataset. You need large numbers to achieve “statistical power” and “statistically significant” results – often those large sample sizes are difficult to achieve, especially for budgetless or self-funded research such as a Masters dissertation or thesis.

Quantitative techniques require a bit of practice and understanding (often more understanding than most people who use them have). And not just to do, but also to read and interpret what others have done, and spot the potential flaws in their research design (and your own). If you come from a statistics background, this won’t be a problem – but most students don’t have this luxury.

Finally, because of the assumption of objectivity (“it must be true because its numbers”), quantitative researchers are less likely to interrogate and be explicit about their own biases in their research. Sample selection, the kinds of questions asked, and the method of analysis are all incredibly important choices, but they tend to not be given as much attention by researchers, exactly because of the assumption of objectivity.

Mixed methods: a happy medium?

Some of the richest research I’ve seen involved a mix of qualitative and quantitative research. Quantitative research allowed the researcher to paint “birds-eye view” of the issue or topic, while qualitative research enabled a richer understanding. This is the essence of mixed-methods research – it tries to achieve the best of both worlds .

In practical terms, this can take place by having open-ended questions as a part of your research survey. It can happen by having a qualitative separate section (like several interviews) to your otherwise quantitative research (an initial survey, from which, you could invite specific interviewees). Maybe it requires observations: some of which you expect to see, and can easily record, classify and quantify, and some of which are novel, and require deeper description.

A word of warning – just like with choosing a qualitative or quantitative research project, mixed methods should be chosen purposefully , where the research aims, objectives and research questions drive the method chosen. Don’t choose a mixed-methods approach just because you’re unsure of whether to use quantitative or qualitative research. Pulling off mixed methods research well is not an easy task, so approach with caution!

Recap: Qualitative vs Quantitative Research

So, just to recap what we have learned in this post about the great qual vs quant debate:

• Qualitative research is ideal for research which is exploratory in nature (e.g. formulating a theory or hypothesis), whereas quantitative research lends itself to research which is more confirmatory (e.g. hypothesis testing)
• Qualitative research uses data in the form of words, phrases, descriptions or ideas. It is time-consuming and therefore only has a small sample size .
• Quantitative research uses data in the form of numbers and can be visualised in the form of graphs. It requires large sample sizes to be meaningful.
• Your choice in methodology should have more to do with the kind of question you are asking than your fears or previously-held assumptions.
• Mixed methods can be a happy medium, but should be used purposefully.
• Bathwater temperature is a contentious and severely under-studied research topic.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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thanks much it has given me an inside on research. i still have issue coming out with my methodology from the topic below: strategies for the improvement of infastructure resilience to natural phenomena

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Qualitative vs Quantitative Data: Definitions, Analysis, Examples

If you are involved in statistics, marketing or data science, it is essential to know what is the difference between qualitative and quantitative data and analysis.

On this page:

• Qualitative vs quantitative data : definition, examples, characteristics, contrast, similarities, and differences.
• What is quantitative data analysis? Steps and types.
• What is qualitative data analysis? Steps and types.
• Comparison chart in PDF (infographic).

What is quantitative data?

Quantitative data seems to be simpler to define and identify.

Quantitative data is data that can be expressed as a number or can be quantified. In other words, quantitative data can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented with a wide variety of statistical types of graphs and chards such as line, graph, bar graph, scatter plot , box and whisker plot and etc.

Key characteristics of quantitative data:

• It can be quantified and verified.
• Data can be counted.
• Data type: number and statistics.
• It answers questions such as “how many, “how much” and “how often”.

Examples of quantitative data:

• Scores on tests and exams e.g. 85, 67, 90 and etc.
• The weight of a person or a subject.
• The number of hours of study.
• Your shoe size.
• The square feet of an apartment.
• The temperature in a room.
• The volume of a gas and etc.

Types of quantitative data:

There are 2 general types of quantitative data:

• Discrete data – a count that involves integers. Only a limited number of values is possible. The discrete values cannot be subdivided into parts. For example, the number of children in a school is discrete data. You can count whole individuals. You can’t count 1.5 kids.
• Continuous data –  information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value. For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc. More on the topic see in our post discrete vs continuous data .

What is qualitative data?

As you might guess qualitative data is information that can’t be expressed as a number and can’t be measured.

Qualitative data consist of words, pictures, observations, and symbols, not numbers. It is about qualities.

Qualitative data is also called categorical data . The reason is that the information can be sorted by category, not by number. Qualitative data is analyzed to look for common themes.

Key characteristics of qualitative data:

• It cannot be quantified and verified.
• Data cannot be counted.
• Data type: words, objects, pictures, observations, and symbols.
• It answers questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

• Your socioeconomic status
• Colors e.g. the color of the sea
• The Smell e.g. aromatic, buttery, camphoric and etc.
• Your favorite holiday destination such as Hawaii, New Zealand and etc.
• Names as John, Patricia,…..
• Sounds like bang and blare.
• Ethnicity such as American Indian, Asian, etc.

Quantitative Data Analysis: Meaning, Steps, and Types

Quantitative data analysis ends with easy to understand and quantifiable results. You can analyze it in many different ways. But before starting the analysis you have to define the level of measurement involved in the quantitative data.

Let’s see the steps in the process of analyzing quantitative variables. It will help us to see better the difference between qualitative and quantitative data analysis.

Step 1: Identify the level of measurement 

There are 4 scales/levels of measurement:

• Nominal –  data scales used simply for labeling variables, without quantitative value. The nominal data just name a thing without applying it to an order. Even though we can use the numbers, they do not denote quantity. Examples of nominal data: hair color (Blonde, Brown, Brunette, etc.).
• Ordinal . Ordinal data is placed into some kind of order by their position on the scale. They often indicate superiority. Example of ordinal data: the first, second and third person in a competition. To understand better see our post nominal vs ordinal data .
• Interval –  numerical scales that show information about an order. In interval scales, the intervals between each data value are the same. A popular example here is the temperature in centigrade, where, for instance, the interval between 930C and 950C is the same as the distance between 1060C and 1080C. However, there isn’t a starting point in the interval scales. See more interval data examples .
• Ratio –  not only show order and have equal intervals, but they can also have a value of zero.

Identifying the levels of measurement where a dataset falls under, will help you decide whether or not the data is useful in making calculations. The scales of measurement are very important because they determine the types of data analysis that can be performed.

The best way of doing that is with specialized data software.

As you have the raw data, you cannot just sit and look at it. You need to take actions to identify some patterns or to visualize what the data is showing.

This is where descriptive statistics and inferential statistics come to play.

Step 2:  Perform descriptive statistics

Descriptive statistics are used to describes and summarizes basic features of a data set. Commonly used descriptive statistics are:

• Central tendency (mean, mode, and median).
• Percentages.
• Dispersion (range, quartiles, variance, and standard deviation)
• Distribution.

Step 3:  Perform inferential statistics

Inferential statistics are used to draw conclusions and trends about a large population based on a sample taken from it. Inferential statistics study the relationships between variables within a sample.

Inferential statistics allow you to test different hypotheses and to generalize the gained results to the population as a whole.

Key inferential techniques, methods, and types of calculations are:

• Linear regression models
• Logistic regression
• Analysis of Variance (ANOVA)
• Analysis of Covariance (ANCOVA)
• Statistical significance (T-Test)
• Correlation analysis

Step 4:  Define statistical significance

Finally, you need to look for statistical significance. Statistical significance is captured through a ‘p-value’, which evaluate the probability that your discovering for the data are reliable results, not a coincidence. The lower the p-value, the more confident you can be that your findings are reliable.

As you see when it comes to quantitative data analysis, there are many techniques and methods you can use.

The next step in our post for the difference between qualitative and quantitative data is to see what qualitative data analysis involves.

Qualitative Data Analysis: Definition, steps, and types

It is harder to perform Qualitative Data Analysis (QDA) in comparison with quantitative one.

QDA includes the processes and methods for analyzing data and providing some level of understanding, explanation, and interpretation of patterns and themes in textual data.

Qualitative data analysis is very important because it allows data sciences and statisticians to form parameters for observing and analyzing larger sets of data.

For example, if a company need to identify the diversity of its personnel, it would look at qualitative data such as ethnicity and race of its employees.

For comparison, quantitative data, in this case, could be the frequency of workers to belong to those ethnicities and races.

In general, the qualitative data analysis has the following steps:

Step 1:  Become familiar with your data

As a data scientist or researcher, you have to read and re-read the data, record detailed notes and impressions, and deciding which pieces of data possess value.

Step 2:  Define the key questions that they need to answer through the QDA

Each QDA has specific questions, problems or topics. Find out which questions do you need to answer.

Step 3:   Reduce and code the data into themes

This means to create categories and subcategories. These categories are very likely to get bigger as you work through your data. The list of build themes represents your first set of codes.

Step 4:   Search for patterns and connections

This step involves looking for the relative importance of data, identifying relationships between data sets or themes, and trying to find explanations from the available data.

Step 5:  Interpret the data and draw conclusions

After you identify the themes, connections, and patterns, now you need to assign meaning and importance to the data.

It is very likely to find out much more than you could need, so you will have to decide what is most significant data and results.

Note: qualitative data do not drive conclusions and generalizations across a population. This is an important difference between qualitative and quantitative data analysis. In contrast, quantitative analysis can lead to conclusions or trends about a large population based on a sample taken from it.

There is a range of approaches to qualitative data analysis. Some of the key approaches are:

• Content analysis – a technique to make inferences by interpreting and coding textual information (e.g., documents, graphics, oral communications).
• Thematic analysis –  a widely-used QDA method that involves grouping the data into themes to define patterned meaning across a dataset.
• Discourse analysis – includes a group of approaches for analyzing written or vocal interactions or any significant semiotic event. The method focuses on the social context in which the communication happened. It seeks to understand how people express themselves.
• Grounded theory – allows you to look for latent social patterns and structures.

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Working in data management area involves familiarizing yourself with the suitable software solutions. There is a wide range of tools, dashboards, and programs that you can use to make the whole data analysis process effective and profitable.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

Prevent plagiarism, run a free check.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Introduction

Quantitative data

Qualitative data analysis, forms of qualitative data, limitations of qualitative data, how to balance qualitative and quantitative research.

• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Qualitative vs. quantitative research: Methods & data analysis

It might be easy to get bogged down in a "qualitative vs. quantitative data" debate, particularly when quantitative and qualitative research seem like very different things. However, both qualitative and quantitative data have their uses in research. Hence, researchers need to know what each approach has to offer before deciding which research approach and methods are best for them.

Over time, your research might rely on both qualitative and quantitative data. It's important not to treat one as more important or better than the other. Instead, it will benefit your research if you know when and how to use both forms of data to address your research inquiries.

Quantitative data refers to any numerical data that can be used in statistical analysis or experimental research.

Researchers in quantitative research often collect data and conduct analysis to make generalizable conclusions about a particular phenomenon or subject. Survey researchers can sample a portion of a population and assert whether the survey results are indicative of the perspectives of the whole population.

Collecting quantitative data

Generally, quantitative data collection methods are more straightforward than their qualitative data counterparts. Suppose your research question involves measuring foot traffic around a city. In such a project, a researcher could place volunteers at selected places and have them count how many times people cross a street in their view.

The volunteers' counts make the quantitative data needed to answer the research questions. Making assertions about the foot traffic in different places is a relatively simple task, given that the numbers are easily collected and readily available for comparison.

Forms of quantitative data

Quantitative data collection relies on structure and a clear understanding of what the numerical values mean to the research. Quantitative researchers can readily take a spreadsheet of test scores, for example, to generate descriptive statistics and inferential statistics. The shape of that spreadsheet (e.g., rows and columns) and its content (e.g., numerical data) ultimately make analyzing quantitative data feasible.

Limitations of quantitative data

Some phenomena cannot be reduced to mere numbers. For example, quantitative data may tell you the value of a particular product, but it faces significant challenges in helping explain a product's inherent beauty or effectiveness.

Such concepts can be difficult for quantitative data to define. After all, what is beautiful to someone will be less so to someone else, and vice versa.

Quantitative research may also face limitations in measuring people's perspectives. Survey research often relies on Likert scales or rating scales asking respondents to rate something on a numerical scale (e.g., from one to five or one to ten).

However, is one respondent's idea of a "4" on a five-point scale the same as another’s idea of a "4" on this same scale? Moreover, subjective concepts are especially difficult to capture with numerical data.

Qualitative research tends to look at the detail of a phenomenon rather than its numerical value. Qualitative research methods allow for theoretical development or exploration of a relatively unfamiliar phenomenon.

Think about a beautiful song. It might be beautiful because of the melody, singer, lyrics, or perhaps some combination of these and other factors. Collecting quantitative data on each aspect (e.g., "Give the melody of the song a score between one and five") might allow for some statistical analysis of a song.

However, what exactly does someone mean when they give a high rating for a song's melody or lyrics? Do they mean the melody is relaxing, inspiring, or something else? Quantitative approaches alone are insufficient in allowing researchers to determine what people think is a "beautiful melody."

Coding qualitative data

Qualitative research relies on methods like interviews to explore social phenomena beyond the use of numbers. ATLAS.ti lets researchers code qualitative data , summarizing large sets of information more succinctly so that gathering insights becomes easier.

When someone speaks at length about a song's melody being "relaxing," a researcher can apply the code "relaxing melody" to an entire segment of text in ATLAS.ti. That way, analyzing the data means looking at brief codes instead of lengthy paragraphs or pages where the meaning might be unclear.

Developing theoretical insights

Qualitative analysis can also prompt us to look at a phenomenon from new and different angles. A researcher may conduct in-depth interviews at places where individuals think a song is beautiful, like at a live concert.

The findings may not fit our prior understanding of a beautiful song, meaning quantitative research wouldn't likely capture it. Statistical analysis might have difficulty reaching a reliable conclusion since different people might have different definitions of what makes a beautiful song.

As a result, the potential for qualitative research to further develop theory cannot be understated, particularly when it allows researchers to document new insights that quantitative methods might miss. While the qualitative research process can be daunting, it has the potential to provide more detail than a simple statistical analysis can.

Put your data to work for you! Quantitative and qualitative data analysis at your fingertips!

Click here for a free trial of ATLAS.ti.

Qualitative studies often draw from the following data collection methods:

• surveys or questionnaires
• in-depth interviews
• focus groups
• observations
• document collection

This is not an exhaustive list, as any unstructured data that can be organized might be considered qualitative data.

What is especially important is that qualitative data is not confined to text. Most forms of information can be analyzed for more insightful discussion. ATLAS.ti allows researchers to code major forms of qualitative data , including images, audio, and video . With the structure provided by coding, researchers can identify recurring themes and patterns in all forms of qualitative data.

Unlike quantitative data, which is often readily available in spreadsheets, qualitative data tend to lack an easily defined structure that facilitates data analysis . In addition, interpreting non-numerical data can be challenging, while clear formulas exist that researchers can follow to compare quantitative values.

Moreover, in semi-structured interviews or focus groups , researchers may ask follow-up questions that can't easily be predicted. An interesting answer may lead to deeper questions to search for more in-depth insights.

The need for the interviewer to pursue deeper answers can impede the organization of data into neat rows and columns. However, it is important to organize the data so that different meanings that emerged across participants or data sources can be assessed. Researchers often need to take time to reorganize their data to facilitate interpretation .

Moreover, interpreting non-numerical data is a significant challenge for qualitative researchers. The relative quantitative value of different things can be relatively easy to interpret.

If someone takes the temperature of New York and the temperature of Chicago on the same day and gets two different values, asserting that one city is warmer than the other would be uncontroversial. After all, one need only get a numerical value representing the temperature in each city to come to a fairly straightforward conclusion.

However, people may disagree about what makes a city interesting or exciting. To take from our example about music, people may even disagree about whether the visual or performative elements of music should be considered. Thus, the researcher needs to clarify the potential differences in understanding between people.

Analyzing qualitative data to answer such research questions requires transparency in analysis. Researchers analyzing socially constructed, subjective concepts should clearly define their concepts so their audiences understand the data analysis.

People can make the mistake of choosing qualitative or quantitative data exclusively. Both approaches are useful in determining cause-and-effect relationships and drawing conclusions based on rigorous analyses.

Choosing research questions

Your inquiry will determine whether quantitative data or qualitative data are more appropriate for your research. In any study, think about how your research question guides what data to collect and how to analyze it.

A quantitative research question seeks to confirm something based on theory that researchers have already developed. On the other hand, a qualitative research question looks at something unfamiliar for which theory does not yet exist to explain it.

In the end, the research question you ask is more important than deciding whether one approach is generally better than the other. By clearly defining what you want to know, you will have a better understanding of what methods will work best for your research project.

Filling research gaps

Quantitative data collection methods can miss nuances that cannot be measured statistically. In contrast, qualitative data collection methods may lack the necessary precision in research contexts where numerical assessment is required. Ultimately, a multitude of data collection and analysis methods may address your research inquiry better than any singular approach.

In situations where a more comprehensive understanding is required, you may want to consider a mixed methods study that collects and analyzes quantitative and qualitative data. A mixed methods approach that employs both quantitative and qualitative methods can be more time-consuming and cumbersome, but the multiple approaches work hand in hand so that each approach covers the shortcomings of the other.

Advancing the overall research agenda

When choosing whether to collect quantitative data, qualitative data, or both, the bigger question is what you want to know, which determines the data collection methods and data analysis that are most effective for your research project. Researchers can benefit from understanding the strengths and weaknesses of quantitative and qualitative data and deciding how both can benefit their research.

Qualitative vs. quantitative data? ATLAS.ti helps you make sense of both

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Qualitative data and quantitative data are two different but complementary types of information that together are essential components of a comprehensive approach to research. Neither quantitative data nor qualitative data can provide as full and nuanced a picture on its own as the two can when used together.

• Qualitative Data— Descriptive, non-numerical data that captures the qualitative elements of a subject, providing context and depth by uncovering deeper insights into human experiences, emotions, and perceptions.
• Quantitative Data— Numerical data that can be analyzed and measured mathematically to provide a foundation for statistical modeling and rigorous analysis by facilitating objective measurement, comparison, and the detection of patterns or trends.

Businesses that use data to fuel decision-making, better understand customer behavior, or analyze the market need to incorporate both quantitative and qualitative data into their overall data management strategy.

Table of Contents

Comparison Chart: Qualitative vs. Quantitative Data

This comparison chart gives a quick summary of the fundamental differences between qualitative and quantitative data, including their nature, purpose, application, and methodologies for data analytics .

Quantitative data consists of quantifiable quantities and numerical values, allowing for accurate analysis and statistical inference. It is used in scientific research and other areas where objective measurement and statistical validation are important.

What is Qualitative Data?

Qualitative data is data that cannot be objectively measured or counted or data that expresses the subjective and interpretive qualities of something. It provides a more detailed view of a subject by getting into its inherent aspects and characteristics.

It includes non-number observations like descriptions, narratives, and themes, providing a rich context beyond simple numerical representation. This form of data is especially useful in the social sciences and humanities, where capturing the complexities of human behavior, emotions, and perceptions is critical.

Benefits of Using Qualitative Data

Qualitative data can capture changing attitudes and ideas within a target group, offering insights into the dynamic nature of perspectives over time. It provides flexibility, allowing for in-depth explanations of non-numerical facts. Some of the most important benefits include the following:

• Captures change— Qualitative research excels in capturing changing attitudes and evolving ideas within a target group, revealing the fluid character of perspectives across time.
• Is less restrictive— Qualitative approaches are not restricted by the same constraints as quantitative methods, allowing for in-depth explanations that disclose more about non-numerical facts and providing insights numbers alone cannot convey.
• Allows for flexible collection and analysis— The process of gathering and analyzing qualitative data provides for a far more flexible approach, allowing alterations to approaches and methodologies dependent on the research’s ongoing dynamics.
• Encourages speculative investigations— Qualitative approaches allow researchers to be significantly more speculative about the topics they choose to study, encouraging inquiry and discovery in areas that may not be immediately obvious.

Limitations of Qualitative Data

Qualitative data has its constraints. For example, questions designed by researchers can introduce researcher-induced bias that affects impartiality; similarly, using volunteers may bring self-selection bias, which can affect sample representativeness. The most common limitations include the following:

• Can limit scale— Common in qualitative research, small sample sizes limit the ability to generalize findings to wider groups.
• Leaves room for bias— Researchers have an important role in creating questions and selecting relevant data, which may introduce biases that affect outcomes and jeopardize impartiality. Similarly, relying on volunteers for interviews or focus groups might result in self-selection bias, skewing findings toward those most likely to participate.
• Subject to environmental variabilities— Qualitative research may gather persons in artificial surroundings, which may modify the way participants submit data as a result of the changing context. This change in context may alter participant reactions and cause deviations from usual behaviors.
• Can be affected by question choice— It might be difficult to guarantee that the questions asked are the most effective or appropriate for the research topic, especially when time is limited. Incomplete or less informative replies may arise from poorly worded questions, lowering the overall quality of the study.

When To Use Qualitative Data

Qualitative data is commonly used in survey research, interviews, and observational studies, as it dives deeply into participant motives, attitudes, and actions. Open-ended questions in surveys capture complex replies, whereas interviews allow for direct involvement for a deeper understanding.

Systematic observation in natural environments is used in observational research. Thematic analysis of qualitative data provides a broader insight into the human experience, supplementing the numerical focus of quantitative data in survey research. In addition, qualitative data is useful for finding complicated and context-specific information that quantitative metrics may miss. It enables researchers to investigate unexpected topics and modify their questions in response to participant replies.

Because qualitative analysis is subjective, it fosters a more holistic and nuanced interpretation of data , which contributes to a more thorough knowledge of the phenomena being studied.

How To Measure Qualitative Data

Measuring qualitative data involves actively understanding information gathered from many sources, such as evaluating visual material like photos and videos, summarizing open-ended survey replies, and extracting insights from in-depth interviews. Researchers decode nuances conveyed by visual cues and distill key themes emerging from survey participants’ input.

This proactive approach guarantees that the complexities inherent in qualitative data are adequately examined and relevant patterns are found, providing a comprehensive knowledge of the phenomena under investigation.

4 Qualitative Data Examples

Here are four common examples of qualitative data:

• Images— Visual representations of information, such as pictures, paintings, or drawings that allow researchers to identify patterns, emotions, and visual signals in addition to textual and numerical data in a variety of domains.
• Transcript from Videos— Video transcripts offer a comprehensive understanding of human communication, capturing both verbal and non-verbal cues, providing valuable insights for psychology, sociology, and communication studies.
• Interview Recordings— Interview recordings offer a unique method for acquiring qualitative data, exposing participants’ viewpoints and feelings through vocal expressions, intonations, and ambient noises.
• Open-ended survey responses— Written or vocal replies allow respondents to express themselves freely, with no predefined response possibilities.

What is Quantitative Data?

Quantitative data is measurable information expressed numerically—as numbers, percentages, or fractions. Quantitative data enables rigorous statistical analysis , making it indispensable in scientific research and corporate analytics. When gathered through methods such as surveys and tests, it allows for exact measurements, hypothesis testing, and trend detection.

Because of its neutrality and compatibility with statistical methodologies, it is an indispensable tool for generating meaningful findings and guiding data-driven decisions. Graphs and charts, for example, improve the accessibility and transmission of complicated quantitative information.

Benefits of Using Quantitative Data

The main advantage of quantitative data is its objectivity—precise numbers help statistical analysis, inform decision-making processes, and contribute to evidence-based research in a variety of areas. Some of the most common benefits include the following:

• Is objective and replicable— Objectivity means that the information gathered is impartial, not affected by personal ideas or perception; researchers using the same methodologies should reach comparable findings when examining the same datasets.
• Provides statistical stringency— Being subjected to rigorous statistical analysis allows a systematic and objective examination of data patterns and linkages, which improves the validity of study findings.
• Results can be generalized— Generalizability involves the ability to extend research findings from a specific sample to a larger population.
• Facilitates decision-making— To analyze trends, evaluate the impact of actions, and drive strategic planning, decision-makers rely on statistical insights produced from systematic analysis.

Limitations of Quantitative Data

Quantitative data has limits in capturing the complexities and variety of events—particularly in subjective situations—and can produce deceptive findings when emotions are involved because they are difficult to quantify. The most common limitations include the following:

• Favors simplicity— Quantitative data may oversimplify things, ignoring their complexity and richness and leading to a loss of subtlety and a shallow comprehension of the subject.
• Neglects subjective nuances— It tends to disregard in-depth experiences and subjective interpretations in circumstances involving test-takers.
• Results can be misleading— Due to the difficulty of quantifying subjective experiences, quantitative data might produce deceptive conclusions. This difficulty highlights the importance of using a more sophisticated approach when examining emotional reactions.
• Open to misrepresentation— Because quantitative data is numerical, it is prone to deception or misinterpretation if not thoroughly studied. This vulnerability highlights the significance of meticulous data interpretation to prevent forming incorrect conclusions.
• Can be overgeneralized— Quantitative data tends to simplify complex phenomena, leading to sweeping conclusions. This underlines the importance of cautious interpretation and appreciation of the multifaceted character of the issues being investigated.

When To Use Quantitative Data?

Quantitative data gives you the “what,” or the measurable information that indicates current processes and future outcomes. It is particularly useful when numerical precision is required for analysis, decision-making, or performance evaluation. Quantitative data provides an organized and objective framework for quantifying events, assessing statistical correlations, and comparing numerical variables.

It comes particularly handy when the emphasis is on numerical patterns, trends, and statistical significance. Quantitative data lends itself well to large-scale investigations, surveys, and experiments, allowing for the systematic collection and analysis of numerical information across varied groups.

Its strengths shine in circumstances that need clarity, objectivity, and the capacity to make exact, quantifiable conclusions, giving it a foundation for evidence-based research and informed decision-making in a variety of sectors.

How To Measure Quantitative Data

Quantitative data may be measured using a variety of approaches that are adapted to individual research needs. Indices and scales provide a systematic technique for studying complicated constructions by assigning numerical values to observable variables. Questionnaires collect data effectively through written and organized questions with preset responses, whereas interviews provide in-depth examination through either consistent or flexible questioning.

In addition, experiments in controlled conditions modify variables systematically to demonstrate cause-and-effect linkages. Controlled observations entail monitoring people in controlled environments to comprehend their answers. Direct measurement uses purpose-built equipment such as thermometers to acquire accurate and objective data on certain constructions. Each technique provides a distinct strategy for collecting and analyzing quantitative data in diverse research situations.

3 Quantitative Data Examples

Quantitative data is often used in scientific research, marketing analysis, trends research, and in various contexts and scenarios where numerical precision, statistical analysis, and objective measurement are crucial. Here are three common examples of quantitative data:

• Experiments— The manipulation and measurement of variables in a controlled environment to observe and analyze outcomes numerically.
• Surveys— Gathering responses to structured questions from a sample population.
• Measurements, counts, and calculations— Physical data pieces that lay the groundwork for in-depth study, resulting in a clear and full comprehension of the subject.

Key Similarities and Distinctions of Qualitative and Quantitative Data

While quantitative and qualitative data are unique, they complement one another in research, providing a holistic picture. When used in collaboration—referred to as mixed methods research—they can triangulate findings for increased validity.

Quantitative data is statistically precise and generalizable, exposing trends, but qualitative data adds depth by capturing human experiences and contextual subtleties. This integrated method enables researchers to approach problems from a variety of perspectives, enhancing the overall narrative and leading to a more comprehensive understanding of complicated phenomena.

Qualitative Vs. Quantitative: Can Data Be Both?

Data can potentially be modified to switch from qualitative data to quantitative data, or vice-versa, but a single piece of data cannot be considered both qualitative and quantitative at the same time. Many types of qualitative data get confused with quantitative data specifically.

Categorical data is one type of qualitative data that looks like it has turned into quantitative data because the researcher has divided the data into groups and each of the groups and the quantities within them can now be counted. However, categorical data is not considered quantitative data, because the researcher subjectively defines the groups and there is still plenty of non-numeric data available to analyze within the data set.

Bottom Line: Qualitative Data Vs. Quantitative Data

Both qualitative and quantitative data are valuable, and a comprehensive approach to research may include both for a more thorough and nuanced understanding. While quantitative data is useful for statistical precision and broad trends, qualitative data adds depth by capturing the complexity of human experiences and context-specific subtleties.

To learn more about how organizations use data—both qualitative and quantitative—to inform their approach to making choices across all aspects of their business, read our guide to the top best practices for data-driven decision-making .

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Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Quantitative vs Qualitative Data

Roxana elliott | nov. 17, 2020 | 3 min. read.

Quantitative and qualitative research methods differ in several ways, including how quantitative and qualitative data is collected and analyzed and the type of insights that each method can provide. While researchers can combine quantitative and qualitative methods to more fully answer their research questions, each has unique characteristics that should be considered throughout the lifecycle of a research project. Jump to GeoPoll’s cheat sheet on qualitative vs quantitative research . 

Difference Between Quantitative and Qualitative Data

The primary difference between quantitative and qualitative data is that quantitative  data represents data that can easily be measured or quantified , such as the number of people who have bought a product.  Qualitative  data represent opinions or feelings and cannot be represented by a numerical statistic such as an average.

For example, if a survey asked 500 respondents the question “Did you buy ice cream today?”, and 300 responded ‘yes’ while 200 responded ‘no’, we would know that 300/500 or 60% bought milk, a quantitative fact. If the same survey asked an open-ended follow-up question: “Why did you choose the brand of ice cream you bought?” you would receive qualitative insights that are unique to each respondent. One person may say, ‘I liked the packaging and label colors’ while another may state, ‘It was the first one I saw on the shelf.’ These descriptive insights cannot easily be quantified into numbers, so they are qualitative.

Qualitative vs Quantitative Analysis

Another difference between quantitative and qualitative research is how data is analyzed. While quantitative data can be analyzed statistically and calculated into averages, means, and other numerical data points, qualitative data analysis involves a more complex system.

To glean insights from qualitative data , researchers conduct a manual analysis of datasets and often code responses into categories. For example, to analyze focus group data, researchers could review transcripts or recordings and group similar sentiments together into categories. Due to this manual process, qualitative data analysis is a longer and more labor-intensive process than quantitative data analysis, which is another factor to keep in mind when deciding what type of data to collect.

While some methods such as focus groups typically collect qualitative data, other methods such as surveys often collect quantitative and qualitative data within one survey instrument, as outlined below.

Quantitative Data Examples

• “Did you go to work today? 1) Yes 2) No”
• “What is your favorite flavor of ice cream? 1) Vanilla 2) Chocolate 3) Cookie Dough 4) Peppermint 5) Chocolate chip”
• “Which of the following products did you buy last week? 1) Toothpaste 2) Soap 3) Vegetables 4) Meat 5) Grains 6) Bread”
• “Please rank the statement ‘I enjoy ice cream’ from 1: Strongly disagree to 5: Strongly agree”
• “How much money did you spend at the grocery store today? Please respond with a dollar amount”
• Observations that can be categorized or quantified, such as the number of times a person checks their phone in a given time. These observations often take place in a controlled environment.

As all of these question types collect data that fit into set categories or can be calculated into averages and other statistics, they are quantitative.

Qualitative Data Examples

Qualitative data can also be collected through certain types of survey questions, in addition to interviews and focus groups. Examples of qualitative data include:

• “Why is cookie dough your favorite flavor of ice cream?”
• Unstructured and semi-structured interviews allow topics and questions to flow naturally, rather than only asking questions from a set question list in a specific order.
• In focus groups, multiple people have a discussion (in-person or via an online or mobile-based chat group) facilitated by a trained moderator who gives prompts to start conversations.
• Researchers can gather qualitative data through unstructured observations, such as observing participants as they partake in certain activities such as shopping.
• Reviewing documents to better understand a particular topic or categorize elements of documents is a type of qualitative research.

Data collected from these methods and question types do not provide numerical statistics but instead, give insights that are often longer and more detailed than their quantitative counterparts.

When Should I Use Quantitative or Qualitative Research?

Qualitative research is often conducted at the beginning of a study when researchers are looking to gather broad, unstructured information on a topic to create a hypothesis, which can then be more clearly answered by quantitative research. Qualitative data collected through unstructured interviews or focus groups can also inform the development of a more structured questionnaire administered to a larger group.

For example, a focus on different ice cream brands may uncover that participants generally consider price and packaging first. That information can then be inputted into a quantitative question: “Which is more important to you when buying ice cream? 1) Price 2) Packaging” administered to a nationally representative sample.

Qualitative data may also be used as part of a mixed-methods research study to add additional context to quantitative data. A researcher may administer both a quantitative questionnaire and conduct a qualitative analysis of interviews with subject-matter experts to form a more robust conclusion.

Surveys can also be split between qualitative and quantitative; Many surveys are mostly quantitative questions that can be quickly analyzed, plus one or two qualitative questions that provide deeper insights into the topic being studied.

Quantitative vs Qualitative Data: Definitions and Uses Cheat Sheet

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• Tags qualitative data , qualitative methodologies , Qualitative research , qualitative vs quantitative , quantitative data , quantitative research , survey methodology

Qualitative vs. quantitative data in research: what's the difference?

If you're reading this, you likely already know the importance of data analysis. And you already know it can be incredibly complex.

At its simplest, research and it's data can be broken down into two different categories: quantitative and qualitative. But what's the difference between each? And when should you use them? And how can you use them together?

Understanding the differences between qualitative and quantitative data is key to any research project. Knowing both approaches can help you in understanding your data better—and ultimately understand your customers better. Quick takeaways:

Quantitative research uses objective, numerical data to answer questions like "what" and "how often." Conversely, qualitative research seeks to answer questions like "why" and "how," focusing on subjective experiences to understand motivations and reasons.

Quantitative data is collected through methods like surveys and experiments and analyzed statistically to identify patterns. Qualitative data is gathered through interviews or observations and analyzed by categorizing information to understand themes and insights.

Effective data analysis combines quantitative data for measurable insights with qualitative data for contextual depth.

What is quantitative data?

Qualitative and quantitative data differ in their approach and the type of data they collect.

Quantitative data refers to any information that can be quantified — that is, numbers. If it can be counted or measured, and given a numerical value, it's quantitative in nature. Think of it as a measuring stick.

Quantitative variables can tell you "how many," "how much," or "how often."

Some examples of quantitative data :  

How many people attended last week's webinar? 

How much revenue did our company make last year? 

How often does a customer rage click on this app?

To analyze these research questions and make sense of this quantitative data, you’d normally use a form of statistical analysis —collecting, evaluating, and presenting large amounts of data to discover patterns and trends. Quantitative data is conducive to this type of analysis because it’s numeric and easier to analyze mathematically.

Computers now rule statistical analytics, even though traditional methods have been used for years. But today’s data volumes make statistics more valuable and useful than ever. When you think of statistical analysis now, you think of powerful computers and algorithms that fuel many of the software tools you use today.

Popular quantitative data collection methods are surveys, experiments, polls, and more.

Quantitative Data 101: What is quantitative data?

Take a deeper dive into what quantitative data is, how it works, how to analyze it, collect it, use it, and more.

Learn more about quantitative data →

What is qualitative data?

Unlike quantitative data, qualitative data is descriptive, expressed in terms of language rather than numerical values.

Qualitative data analysis describes information and cannot be measured or counted. It refers to the words or labels used to describe certain characteristics or traits.

You would turn to qualitative data to answer the "why?" or "how?" questions. It is often used to investigate open-ended studies, allowing participants (or customers) to show their true feelings and actions without guidance.

Some examples of qualitative data:

Why do people prefer using one product over another?

How do customers feel about their customer service experience?

What do people think about a new feature in the app?

Think of qualitative data as the type of data you'd get if you were to ask someone why they did something. Popular data collection methods are in-depth interviews, focus groups, or observation.

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What are the differences between qualitative vs. quantitative data?

When it comes to conducting data research, you’ll need different collection, hypotheses and analysis methods, so it’s important to understand the key differences between quantitative and qualitative data:

Quantitative data is numbers-based, countable, or measurable. Qualitative data is interpretation-based, descriptive, and relating to language.

Quantitative data tells us how many, how much, or how often in calculations. Qualitative data can help us to understand why, how, or what happened behind certain behaviors .

Quantitative data is fixed and universal. Qualitative data is subjective and unique.

Quantitative research methods are measuring and counting. Qualitative research methods are interviewing and observing.

Quantitative data is analyzed using statistical analysis. Qualitative data is analyzed by grouping the data into categories and themes.

As you can see, both provide immense value for any data collection and are key to truly finding answers and patterns. 

More examples of quantitative and qualitative data

You’ve most likely run into quantitative and qualitative data today, alone. For the visual learner, here are some examples of both quantitative and qualitative data: 

Quantitative data example

The customer has clicked on the button 13 times. 

The engineer has resolved 34 support tickets today. 

The team has completed 7 upgrades this month. 

14 cartons of eggs were purchased this month.

Qualitative data example

My manager has curly brown hair and blue eyes.

My coworker is funny, loud, and a good listener. 

The customer has a very friendly face and a contagious laugh.

The eggs were delicious.

The fundamental difference is that one type of data answers primal basics and one answers descriptively. 

What does this mean for data quality and analysis? If you just analyzed quantitative data, you’d be missing core reasons behind what makes a data collection meaningful. You need both in order to truly learn from data—and truly learn from your customers. 

What are the advantages and disadvantages of each?

Both types of data has their own pros and cons. 

Advantages of quantitative data

It’s relatively quick and easy to collect and it’s easier to draw conclusions from. 

When you collect quantitative data, the type of results will tell you which statistical tests are appropriate to use. 

As a result, interpreting your data and presenting those findings is straightforward and less open to error and subjectivity.

Another advantage is that you can replicate it. Replicating a study is possible because your data collection is measurable and tangible for further applications.

Disadvantages of quantitative data

Quantitative data doesn’t always tell you the full story (no matter what the perspective). 

With choppy information, it can be inconclusive.

Quantitative research can be limited, which can lead to overlooking broader themes and relationships.

By focusing solely on numbers, there is a risk of missing larger focus information that can be beneficial.

Advantages of qualitative data

Qualitative data offers rich, in-depth insights and allows you to explore context.

It’s great for exploratory purposes.

Qualitative research delivers a predictive element for continuous data.

Disadvantages of qualitative data

It’s not a statistically representative form of data collection because it relies upon the experience of the host (who can lose data).

It can also require multiple data sessions, which can lead to misleading conclusions.

The takeaway is that it’s tough to conduct a successful data analysis without both. They both have their advantages and disadvantages and, in a way, they complement each other. 

Now, of course, in order to analyze both types of data, information has to be collected first.

Let's get into the research.

Quantitative and qualitative research

The core difference between qualitative and quantitative research lies in their focus and methods of data collection and analysis. This distinction guides researchers in choosing an appropriate approach based on their specific research needs.

Using mixed methods of both can also help provide insights form combined qualitative and quantitative data.

Best practices of each help to look at the information under a broader lens to get a unique perspective. Using both methods is helpful because they collect rich and reliable data, which can be further tested and replicated.

What is quantitative research?

Quantitative research is based on the collection and interpretation of numeric data. It's all about the numbers and focuses on measuring (using inferential statistics ) and generalizing results. Quantitative research seeks to collect numerical data that can be transformed into usable statistics.

It relies on measurable data to formulate facts and uncover patterns in research. By employing statistical methods to analyze the data, it provides a broad overview that can be generalized to larger populations.

In terms of digital experience data, it puts everything in terms of numbers (or discrete data )—like the number of users clicking a button, bounce rates , time on site, and more. 

Some examples of quantitative research: 

What is the amount of money invested into this service?

What is the average number of times a button was dead clicked ?

How many customers are actually clicking this button?

Essentially, quantitative research is an easy way to see what’s going on at a 20,000-foot view. 

Each data set (or customer action, if we’re still talking digital experience) has a numerical value associated with it and is quantifiable information that can be used for calculating statistical analysis so that decisions can be made. 

You can use statistical operations to discover feedback patterns (with any representative sample size) in the data under examination. The results can be used to make predictions , find averages, test causes and effects, and generalize results to larger measurable data pools. 

Unlike qualitative methodology, quantitative research offers more objective findings as they are based on more reliable numeric data.

Quantitative data collection methods

A survey is one of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants. An unclear questionnaire can lead to distorted research outcomes.

Similar to surveys, polls yield quantitative data. That is, you poll a number of people and apply a numeric value to how many people responded with each answer.

Experiments

An experiment is another common method that usually involves a control group and an experimental group . The experiment is controlled and the conditions can be manipulated accordingly. You can examine any type of records involved if they pertain to the experiment, so the data is extensive. 

What is qualitative research?

Qualitative research does not simply help to collect data. It gives a chance to understand the trends and meanings of natural actions. It’s flexible and iterative.

Qualitative research focuses on the qualities of users—the actions that drive the numbers. It's descriptive research. The qualitative approach is subjective, too. 

It focuses on describing an action, rather than measuring it.

Some examples of qualitative research: 

The sunflowers had a fresh smell that filled the office.

All the bagels with bites taken out of them had cream cheese.

The man had blonde hair with a blue hat.

Qualitative research utilizes interviews, focus groups, and observations to gather in-depth insights.

This approach shines when the research objective calls for exploring ideas or uncovering deep insights rather than quantifying elements.

Qualitative data collection methods

An interview is the most common qualitative research method. This method involves personal interaction (either in real life or virtually) with a participant. It’s mostly used for exploring attitudes and opinions regarding certain issues.

Interviews are very popular methods for collecting data in product design .

Focus groups

Data analysis by focus group is another method where participants are guided by a host to collect data. Within a group (either in person or online), each member shares their opinion and experiences on a specific topic, allowing researchers to gather perspectives and deepen their understanding of the subject matter.

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So which type of data is better for data analysis?

So how do you determine which type is better for data analysis ?

Quantitative data is structured and accountable. This type of data is formatted in a way so it can be organized, arranged, and searchable. Think about this data as numbers and values found in spreadsheets—after all, you would trust an Excel formula.

Qualitative data is considered unstructured. This type of data is formatted (and known for) being subjective, individualized, and personalized. Anything goes. Because of this, qualitative data is inferior if it’s the only data in the study. However, it’s still valuable. 

Because quantitative data is more concrete, it’s generally preferred for data analysis. Numbers don’t lie. But for complete statistical analysis, using both qualitative and quantitative yields the best results. 

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A perfect digital customer experience is often the difference between company growth and failure. And the first step toward building that experience is quantifying who your customers are, what they want, and how to provide them what they need.

Access to product analytics is the most efficient and reliable way to collect valuable quantitative data about funnel analysis, customer journey maps , user segments, and more.

But creating a perfect digital experience means you need organized and digestible quantitative data—but also access to qualitative data. Understanding the why is just as important as the what itself.

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Data Analysis Methods: Qualitative vs. Quantitative

• Couchbase Product Marketing February 13, 2024

Data analysis is a crucial step in extracting meaningful insights from collected data. Two common approaches to analyzing data are qualitative and quantitative analysis. Each method offers different techniques for interpreting and understanding your findings.

This blog post will further explore different qualitative and quantitative analysis methods, their strengths and limitations, and how to apply them in various research and business contexts. Whether you’re a researcher, analyst, or decision maker, understanding these methods will help you make informed decisions when analyzing data and deriving valuable insights.

What is Data Analysis, and Why is it Necessary?

Data analysis is comparable to a detective looking for evidence to uncover important information. It helps us understand trends and patterns in data that we may not see immediately. Analyzing data allows us to make better decisions, find opportunities, and solve problems. It’s necessary because it helps make sense of the large amounts of data available today. Data would be messy and hard to understand without analysis, but we can find connections, discover abnormalities, and understand the bigger picture by analyzing it.

It also helps us predict the future by looking at past data. Historical data is useful in fields like business, finance, and healthcare. It allows us to predict customer behavior, market trends, and potential risks. With this information, we can plan and prepare for what might happen. Data analysis also improves performance and efficiency. By studying data, we can find areas to fix or improve, making things run smoother and using resources wisely.

What Does the Data Analysis Process Entail?

Data analysis involves several key steps to extract meaningful insights from data. Here’s an overview of the typical data analysis process:

• Objective Definition: Clearly define the objective of the analysis by understanding the specific questions to answer or problems to solve.
• Data Collection and Preprocessing: Gather relevant data from various sources, ensuring accuracy, completeness, and representativeness. Clean the data by removing errors, inconsistencies, or missing values, and preprocess it as needed (e.g., normalization, standardization).
• Exploratory Data Analysis (EDA): Explore the data through visualization, charts, graphs, and summary statistics to identify patterns, trends, or relationships and gain initial insights.
• Data Analysis Techniques: Depending on the data’s objective and characteristics, suitable techniques like descriptive statistics, hypothesis testing, regression, clustering, or classification can be used to analyze data effectively.
• Interpretation and Communication: Analyze the output of the analysis techniques, interpret the findings in the context of the objective, and draw conclusions. Communicate the results effectively using visualizations, reports, or presentations to stakeholders or decision makers.

Throughout the process, it’s important to validate and verify the analysis by checking for consistency, conducting sensitivity analyses, or using peer review. Additionally, the data analysis process often involves iteration, allowing for refinement and improvement based on initial findings or feedback received.

What is the Difference Between Qualitative and Quantitative Data?

Qualitative and quantitative data are two different types of data used in research and analysis. Here are the key differences between them:

Both qualitative and quantitative data have their strengths and applications. They can be used together in mixed-methods research to comprehensively understand a research topic or triangulate findings for more robust conclusions.

Data Analysis Methods

Data analysis methods refer to the techniques and approaches used to analyze and interpret data. These methods vary depending on the type of data you’re analyzing and the research objectives. Two common categories of data analysis methods are qualitative data analysis and quantitative data analysis.

Qualitative Data

Qualitative data analysis involves examining non-numerical or categorical information to uncover patterns, themes, and meanings. Here are some commonly used methods for analyzing qualitative data:

Thematic Analysis : Identifies recurring themes or patterns in qualitative data by categorizing and coding the data.

Content Analysis : Analyzes textual data systematically by categorizing and coding it to identify patterns and concepts.

Narrative Analysis : Examines stories or narratives to understand experiences, perspectives, and meanings.

Grounded Theory : Develops theories or frameworks based on systematically collected and analyzed data, allowing theory development to be guided by the analysis process.

Quantitative Data

Quantitative data analysis involves analyzing numerical data to uncover statistical patterns, relationships, and trends. Here are some commonly used methods for analyzing quantitative data:

Descriptive Statistics : Summarizes dataset features using mean, median, mode, standard deviation, and percentages.

Inferential Statistics : Draws conclusions about a population based on sample data using hypothesis testing, t-tests, and regression analysis.

Data Mining : Discovers patterns and correlations in large datasets using algorithms and statistical techniques.

Experimental Design : Designs controlled experiments to determine causal relationships between variables.

These are just a few examples of the data analysis methods used for qualitative and quantitative data. The choice of method depends on the research objectives, type of data, available resources, and the specific questions to address. Researchers often employ a combination of methods to comprehensively understand the data and draw meaningful conclusions.

Data Analysis Obstacles

You’ll likely encounter obstacles to obtaining accurate and meaningful insights during the data analysis process. Understanding these obstacles is crucial for effective data analysis. Here are some common ones:

Data Quality Issues: Poor data quality can be a significant obstacle. Addressing data quality issues by carefully cleaning and preprocessing your data is essential.

Insufficient or Unrepresentative Data: If the data collected doesn’t cover the relevant variables or lacks diversity, the insights obtained may be limited or biased. 

Lack of Domain Knowledge: Data analysis often requires domain knowledge to interpret the results accurately. Without a thorough understanding of the subject matter, it can be challenging to identify relevant patterns or relationships in the data.

Complexity and Volume of Data: Large and complex datasets can pose processing, analysis, and interpretation challenges. Analyzing such data requires advanced techniques and tools to handle the volume and complexity effectively.

Biases and Assumptions: Biases and assumptions made during data analysis can influence the process. Biases can occur at various stages, such as data collection, preprocessing, or analysis. 

Overcoming these obstacles requires careful attention to data quality, ensuring representative data, acquiring domain knowledge, utilizing appropriate tools and techniques, and being mindful of biases and assumptions. By addressing these challenges, data analysts can enhance the reliability and validity of their analysis, leading to more accurate and insightful results.

How to Ensure Data Quality

It’s crucial to prioritize data quality to ensure that insights obtained from data analysis are accurate and reliable. Here are some simple steps to ensure data quality:

• Data Collection Planning: Plan the data collection process carefully. Clearly define the data requirements and variables needed to address the analysis objective.
• Data Cleaning and Validation: Thoroughly clean the collected data to remove errors, inconsistencies, or missing values. Validate the data by cross-checking it against known standards or conducting data verification checks. 
• Data Standardization: Ensure consistency and comparability by converting data into a common format, unit, or scale. 
• Data Integration: If working with multiple datasets, integrate them carefully to ensure coherence and accuracy. You should match variables, resolve inconsistencies, and merge all data correctly.
• Data Documentation: Thoroughly document the data collection and preprocessing procedures. Record data sources, data cleaning steps, transformations applied, and any other modifications made.

By following these steps, data quality can be maintained throughout the analysis process. High-quality data enhances the credibility of the analysis and enables informed decision making based on accurate and trustworthy information.

How Data Analysis Benefits Your Organization

Data analysis offers organizations numerous benefits , helping them improve processes, make informed decisions, and gain a competitive edge. Here are some key advantages of data analysis in clear and simple terms:

Informed Decision Making : Data analysis helps organizations make informed decisions by providing valuable insights and identifying trends and patterns in data.

Improved Efficiency and Productivity : By analyzing data, organizations can identify inefficiencies, streamline processes, and allocate resources effectively, improving efficiency and productivity.

Enhanced Customer Understanding : Data analysis enables organizations to gain a deeper understanding of customers and their needs, preferences, and behavior, enabling personalized marketing strategies and better customer service.

Competitive Advantage : Data analysis helps organizations stay ahead of the competition by identifying market trends, monitoring competitors, and uncovering new opportunities.

Risk Identification and Mitigation : Data analysis allows organizations to identify and mitigate risks by analyzing historical data, detecting potential fraud, predicting customer churn, and proactively developing risk management strategies.

In summary, data analysis empowers organizations to make informed decisions, improve efficiency, understand customers, gain a competitive advantage, and mitigate risks, leading to enhanced performance and success.

Key Takeaways 

Data analysis is a powerful process that offers significant benefits to organizations. It enables organizations to improve efficiency, optimize processes, and allocate resources effectively, leading to cost savings and increased productivity. It also helps organizations better understand their customers, tailor strategies, and develop products that meet customer needs, fostering customer satisfaction and loyalty. Furthermore, data analysis provides a competitive advantage by uncovering market trends, monitoring competitors, and identifying new opportunities.

Check out the following resources to learn even more about data analysis:

• What is Big Data Analytics?
• Enterprise Analytics
• Unstructured Data
• Semi-Structured Data
• What is Data Management?
• What is a Data Platform?
• Database vs. Data Warehouse: Differences, Use Cases, Examples
• Couchbase Capella Columnar Adds Real-time Data Analytics Service
• JSON Analytics Product Page
• Posted in: Application Design , Best Practices and Tutorials
• Tagged in: Data Analysis , data analytics

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Qualitative vs. quantitative data: what’s the difference?

Last updated

7 February 2023

Reviewed by

When gathering or analyzing research, remember not all data is created equal. Taking a step back to analyze the core differences between data types will ultimately lead to better research outcomes. Your hypotheses will become clearer, data collection will improve, and your analysis will be aligned with your inputs and the answers you seek.

Learning the difference between qualitative and quantitative data is an important first step—so where should you start?

A key difference in data is the division between quantitative versus qualitative data. Misunderstanding the difference can lead to interpretive errors and faulty judgments.

In contrast, when you clarify and apply those differences to your own data pool, you’ll have more clarity in your research outcomes and feed into better strategic decision-making.

All your data in one place

Analyze your qualitative and quantitative data together in Dovetail and uncover deeper insights

• What are the differences between qualitative vs. quantitative data?

Understanding the fundamental differences between qualitative and quantitative data is crucial. It helps you hone your research to deliver accurate, reliable outcomes.

Looking at the root origin of the words themselves is a good first step toward mastering these differences.

What is quantitative data?

The exact definition of quantitative is “having quantity” or “measurable.”

Quantitative data is exact and objective. You can express it as a number or measurement. It relates to things like:

Quantities of things

Amounts and volumes

Units of weight and measurements

Percentage, fractions, and ratios

Frequencies, rates, or durations

Test scores

Most of these measurements invoke the idea of formats, like Fahrenheit versus Celsius and miles versus kilometers. The definition of what’s being measured can sometimes come into question. Just because something can be expressed as a number doesn’t mean it is its most appropriate form.

Here’s a good measure of quantitative data: if someone asks “what” is being measured, the answer should be extremely simple, concise, and specific. It shouldn’t leave room for interpretation.

In cases where quantitative data fails to account for other important facts or creates contention over its value, there is likely a degree of ambiguity or subjectivity that hasn’t been explored enough. This is what qualitative data can deliver.

What is qualitative data?

Qualitative data is inherently inexact and subjective. It requires interpretation and value judgments.

The origin meaning of qualitative stems from the Latin word qualitas, meaning “a quality, property, or nature.” It relates to the “possession of qualities without reference to quantities.”

This invokes expressions of meaning and value—but it’s still data. So what does qualitative data represent in practical terms?

It represents the qualities and categories of things. Categorization still requires consistency, so qualitative data doesn’t come at the expense of clarity. It’s flexible, but not without reason. The data must be renewed if a category’s boundaries shift.

Consider the following statement: “The gas station is eight miles away as the crow flies, and we can walk straight there with a gas can.”

This directional information depends on how you interpret it qualitatively. Your interpretation will be subjective and based on context. You’ll make a qualitative assessment or value judgment.

Qualitative data requires a description of the quality of things since it’s linguistic instead of numerical. It invites and even requires, interpretation.

Here are some examples of qualitative data:

Responses to direct or indirect questions

Evaluations against a criteria or a set of standards

Opinions or assessments

Open-ended survey responses

Pure qualitative data alone can be viewed as unrepresentative or inconclusive. It can be less helpful to businesses, which require concrete, actionable items.

When qualitative data reveal answers and insight into the topic you are researching, it should be correlated with quantitative data. This combination will show whether qualitative responses and anecdotes translate into hard, quantifiable numbers.

• Quantitative vs. qualitative data gathering

Whether responses are represented numerically or using descriptive language is a question of methodology. It doesn’t alter the subject under investigation.

Note that qualitative and quantitative data are different tools to address different objectives. The innate value of either type of data depends on context.

If you want to determine value in a quantitative sense, you might use a questionnaire with a rating scale to measure the numerical value consumers rate a product or service. The qualitative value would be the positive impact it delivers on their lives or for their families.

Can a question be both qualitative and quantitative?

Whether you quantify or qualify something often depends on what’s being reflected by that data and the intentions for doing so. Consider the following examples of line-blurring challenges between quantitative and qualitative data:

What’s the price of a commodity? Is it the price measured in a currency with fluctuating value, such as a USD paper note? Or is it measured to a fixed weight and standard, such as a US-minted $1 silver coin? Is the value fluctuating or fixed? If it’s fluctuating, a fully quantitative measure requires more information (e.g., $X in USD at 12PM on January 1st, 2023).

What’s the value of a priceless museum artifact? Is it (1) the price the museum paid to obtain it, (2) the market value an experienced appraiser assigns to it, or (3) the experiential value it provides to museum visitors who admire it? The question becomes, which value is being considered? The term “priceless” can be taken literally as a measure of qualitative value, or as a figure of speech meaning “very expensive.”

What’s the ROI of a park or tree? Is it the amount of real estate, crop, or another commodity that can be obtained using that natural resource? Or is it the emotional and health benefits it provides to people who use and enjoy it?

The same thing can be assigned to both quantitative and qualitative data, depending on who is asking and why. Measures that attempt to take both quantitative and qualitative values into account are possible, but only when the people depending on the data (and what it represents) agree on the terms.

What is quantitative research?

Quantitative research involves:

Measuring, counting

Experimenting

Correlating

Calculating

You can consider this data statistically and scientifically reliable when it’s produced with enough rigor. It’s not open to interpretation unless the researcher breaks the rules and conventions of quantitative data collection and analysis.

This data is essential for statistical analysis, but only when objectively and clearly defined. If this isn’t the case and what is being measured is unclear, the data will be unreliable due to the lack of clearly defined parameters. It doesn’t matter how painstaking or impressive the analysis is.

The robustness of quantitative data relies on having a solid dataset, free from outliers and atypical qualifying factors that reflect the real-world conditions of what you’re trying to measure.

If there’s disagreement over quantitative research’s robustness, it’s likely there are unaddressed questions about what the right thing to measure is and why. This must be thoroughly addressed well before measurement takes place.

What is qualitative research?

In qualitative research , data can be:

Grouped and themed

Labeled and coded

Ranked and ordered

Placed in its overall cultural context

How well it relates to quantitative data is important, but it exists independently of it.

Pure qualitative research must be interpreted. You can use it to categorize what will later be measured quantitatively (e.g., a medical condition’s degrees of severity within a medical research paper, like mild, moderate, or severe). When done well, categories, labels, and groups of data clarify the categories to be analyzed qualitatively, and later measured quantitatively.

Qualitative data can lay the foundation for quantitative analysis by creating defined categories in which to explore data later on. Deciding what category boundaries to set is a judgment call, but the category’s relevance is only as good as the quality of the qualitative research that underpins it.

• Applying qualitative vs. quantitative data

Researchers need to be fully aware of what’s being measured and how to agree on which type of data to use to address specific research questions. Otherwise, a measure loses meaning because the definition isn’t consistent or categorical. Just as quantitative data is exact, qualitative data should be clearly defined.

Guessing at what quantitative data represents can lead to confusion, as measures don’t relate to real-world operations. It challenges the purpose of previous data collection efforts. When that purpose changes, it offers an opportunity to reevaluate the right type (or category) of data to analyze.

When to use quantitative or qualitative data

It all begins with a decision between quantifying (measurements) or qualifying (interpretations).

A classic example is the difference between hard and soft skills in the workplace. Soft skills are difficult to measure. Hard skills are easy to measure but much less useful.

You should use qualitative data when:

You have many “why” questions (quantitative methods can address your “what” questions)

The topic is subjective (e.g., why customers feel a certain way about a brand)

Quantitative data can’t be of any further use and you’ve learned all you can from it

You’re no longer clear on what’s being measured by your quantitative data

Categorizing large amounts of non-contextualized or varied data

Your measured data is not verifying your hypotheses

The quantitative data is not lining up with daily realities

Your organization has too many “data silos”

Quantitative data is most important for:

Hard, regular, and discrete data points

Tracking expenses, profits, and other financial matters

Inventory, product, and supply chain management

Timing and scheduling

Keeping tabs on clearly defined KPIs

Delving deeper into an information category that’s already proven its value to you

Sizing and prioritizing opportunities or challenges

Don’t be afraid to switch between qualitative and quantitative data. Just be clear when you do.

Benefits and limitations of qualitative and quantitative research

Numbers are not always the be-all and end-all—especially if a definition is not exact or complete. Fortunately, qualitative research’s disadvantages are typically quantitative research’s advantages, and vice versa. Consider their differences, but don’t set them at odds.

Consider the following question as an example: is the value of a workaholic’s time better defined by its impact on their family or their shareholders? Both parties likely have qualitative and quantitative (respectively) standards in mind, as follows:

Their shareholders consider measurable profits, KPIs, etc.

Their family considers quality time and the strength of their bond

Each party would consider the value of the workaholic’s time differently, according to their purpose. However, these values can easily be seen as conflicting, prompting any of the three parties to think in one of two modes:

Profitability (quantitatively measured)

Quality of time (qualitatively measured)

The limitations and benefits of either data type depend on who’s asking. You’ll need to achieve the right balance between these two modes of data analysis to:

Demonstrably improve the quality of measurements

Naturally, align motives with benchmarks

To get the best answers to your questions, continually review the way you ask them. In the example above, the workaholic could aim to improve the quality of profitable time and the profitability of quality time. This prevents them from seeing hard and soft data as oppositional.

More proactively, data in one category can be used to fill the gaps left by the other category. Today’s apps do just that by synthesizing immense volumes of research data for greater immediate use.

• Making quantitative and qualitative data cohesive

Neither qualitative nor quantitative data alone create the full picture. Used at the right time and for the right reasons, either type of data can offer insight and answers to questions to improve research and wider strategy outcomes.

Favoring quantitative or qualitative data over the other without valid reason risks inaccurate and valueless research. The sky’s the limit when you use them wisely (however much the sky’s edge is open to interpretation…).

Learn more about qualitative research data analysis software

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Quantitative vs. Qualitative Research in Psychology

Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Quantitative vs. Qualitative Research Design: Understanding the Differences

As a future professional in the social and education landscape, research design is one of the most critical strategies that you will master to identify challenges, ask questions and form data-driven solutions to address problems specific to your industry. 

Many approaches to research design exist, and not all work in every circumstance. While all data-focused research methods are valid in their own right, certain research design methods are more appropriate for specific study objectives.

Unlock our resource to learn more about jump starting a career in research design — Research Design and Data Analysis for the Social Good .

We will discuss the differences between quantitative (numerical and statistics-focused) and qualitative (non-numerical and human-focused) research design methods so that you can determine which approach is most strategic given your specific area of graduate-level study. 

Understanding Social Phenomena: Qualitative Research Design

Qualitative research focuses on understanding a phenomenon based on human experience and individual perception. It is a non-numerical methodology relying on interpreting a process or result. Qualitative research also paves the way for uncovering other hypotheses related to social phenomena. 

In its most basic form, qualitative research is exploratory in nature and seeks to understand the subjective experience of individuals based on social reality.

Qualitative data is…

• often used in fields related to education, sociology and anthropology; 
• designed to arrive at conclusions regarding social phenomena; 
• focused on data-gathering techniques like interviews, focus groups or case studies; 
• dedicated to perpetuating a flexible, adaptive approach to data gathering;
• known to lead professionals to deeper insights within the overall research study.

You want to use qualitative data research design if:

• you work in a field concerned with enhancing humankind through the lens of social change;
• your research focuses on understanding complex social trends and individual perceptions of those trends;
• you have interests related to human development and interpersonal relationships.

Examples of Qualitative Research Design in Education

Here are just a few examples of how qualitative research design methods can impact education:

Example 1: Former educators participate in in-depth interviews to help determine why a specific school is experiencing a higher-than-average turnover rate compared to other schools in the region. These interviews help determine the types of resources that will make a difference in teacher retention. 

Example 2: Focus group discussions occur to understand the challenges that neurodivergent students experience in the classroom daily. These discussions prepare administrators, staff, teachers and parents to understand the kinds of support that will augment and improve student outcomes.

Example 3: Case studies examine the impacts of a new education policy that limits the number of teacher aids required in a special needs classroom. These findings help policymakers determine whether the new policy affects the learning outcomes of a particular class of students.

Interpreting the Numbers: Quantitative Research Design

Quantitative research tests hypotheses and measures connections between variables. It relies on insights derived from numbers — countable, measurable and statistically sound data. Quantitative research is a strategic research design used when basing critical decisions on statistical conclusions and quantifiable data.

Quantitative research provides numerical-backed quantifiable data that may approve or discount a theory or hypothesis.

Quantitative data is…

• often used in fields related to education, data analysis and healthcare; 
• designed to arrive at numerical, statistical conclusions based on objective facts;
• focused on data-gathering techniques like experiments, surveys or observations;
• dedicated to using mathematical principles to arrive at conclusions;
• known to lead professionals to indisputable observations within the overall research study.

You want to use quantitative data research design if:

• you work in a field concerned with analyzing data to inform decisions;
• your research focuses on studying relationships between variables to form data-driven conclusions;
• you have interests related to mathematics, statistical analysis and data science.

Examples of Quantitative Research Design in Education

Here are just a few examples of how quantitative research design methods may impact education:

Example 1: Researchers compile data to understand the connection between class sizes and standardized test scores. Researchers can determine if and what the relationship is between smaller, intimate class sizes and higher test scores for grade-school children using statistical and data analysis.

Example 2: Professionals conduct an experiment in which a group of high school students must complete a certain number of community service hours before graduation. Researchers compare those students to another group of students who did not complete service hours — using statistical analysis to determine if the requirement increased college acceptance rates.

Example 3: Teachers take a survey to examine an education policy that restricts the number of extracurricular activities offered at a particular academic institution. The findings help better understand the far-reaching impacts of extracurricular opportunities on academic performance.

Making the Most of Research Design Methods for Good: Vanderbilt University’s Peabody College

Vanderbilt University's Peabody College of Education and Human Development offers a variety of respected, nationally-recognized graduate programs designed with future agents of social change in mind. We foster a culture of excellence and compassion and guide you to become the best you can be — both in the classroom and beyond.

At Peabody College, you will experience

• an inclusive, welcoming community of like-minded professionals;
• the guidance of expert faculty with real-world industry experience;
• opportunities for valuable, hands-on learning experiences,
• the option of specializing depending on your specific area of interest.

Explore our monthly publication — Ideas in Action — for an inside look at how Peabody College translates discoveries into action.

Please click below to explore a few of the graduate degrees offered at Peabody College:

• Child Studies M.Ed. — a rigorous Master of Education degree that prepares students to examine the developmental, learning and social issues concerning children and that allows students to choose from one of two tracks (the Clinical and Developmental Research Track or the Applied Professional Track).
• Cognitive Psychology in Context M.S. — an impactful Master of Science program that emphasizes research design and statistical analysis to understand cognitive processes and real-world applications best, making it perfect for those interested in pursuing doctoral studies in cognitive science.
• Education Policy M.P.P — an analysis-focused Master of Public Policy program designed for future leaders in education policy and practice, allowing students to specialize in either K-12 Education Policy, Higher Education Policy or Quantitative Methods in Education Policy. 
• Quantitative Methods M.Ed. — a data-driven Master of Education degree that teaches the theory and application of quantitative analysis in behavioral, social and educational sciences.

Connect with the Community of Professionals Seeking to Enhance Humankind at Peabody College

At Peabody College, we equip you with the marketable, transferable skills needed to secure a valuable career in education and beyond. You will emerge from the graduate program of your choice ready to enhance humankind in more meaningful ways than you could have imagined.

If you want to develop the sought-after skills needed to be a force for change in the social and educational spaces, you are in the right place .

We invite you to request more information ; we will connect you with an admissions professional who can answer all your questions about choosing one of these transformative graduate degrees at Peabody College. You may also take this opportunity to review our admissions requirements and start your online application today. 

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What Is Qualitative vs. Quantitative Study?

Qualitative research focuses on understanding phenomena through detailed, narrative data. It explores the “how” and “why” of human behavior, using methods like interviews, observations, and content analysis. In contrast, quantitative research is numeric and objective, aiming to quantify variables and analyze statistical relationships. It addresses the “when” and “where,” utilizing tools like surveys, experiments, and statistical models to collect and analyze numerical data.

In This Article:

What is qualitative research, what is quantitative research.

• How Do Qualitative and Quantitative Research Differ?

What’s the Difference Between a Qualitative and Quantitative Study?

Analyzing qualitative and quantitative data, when to use qualitative or quantitative research, develop your research skills at national university.

Qualitative and quantitative data are broad categories covering many research approaches and methods. While both share the primary aim of knowledge acquisition, quantitative research is numeric and objective, seeking to answer questions like when or where. On the other hand, qualitative research is concerned with subjective phenomena that can’t be numerically measured, like how different people experience grief.

Having a firm grounding in qualitative and quantitative research methodologies will become especially important once you begin work on your dissertation or thesis toward the end of your academic program. At that point, you’ll need to decide which approach best aligns with your research question, a process that involves working closely with your Dissertation Chair.

Keep reading to learn more about the difference between quantitative vs. qualitative research, including what research techniques they involve, how they approach the task of data analysis, and some strengths — and limitations — of each approach. We’ll also briefly examine mixed-method research, which incorporates elements of both methodologies.

Qualitative research differs from quantitative research in its objectives, techniques, and design. Qualitative research aims to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified using mathematics. Instead of seeking to uncover precise answers or statistics in a controlled environment like quantitative research, qualitative research is more exploratory, drawing upon data sources such as photographs, journal entries, video footage, and interviews.

These features stand in stark contrast to quantitative research, as we’ll see throughout the remainder of this article.

Quantitative research tackles questions from different angles compared to qualitative research. Instead of probing for subjective meaning by asking exploratory “how?” and “why?” questions, quantitative research provides precise causal explanations that can be measured and communicated mathematically. While qualitative researchers might visit subjects in their homes or otherwise in the field, quantitative research is usually conducted in a controlled environment. Instead of gaining insight or understanding into a subjective, context-dependent issue, as is the case with qualitative research, the goal is instead to obtain objective information, such as determining the best time to undergo a specific medical procedure.

How Does Qualitative and Quantitative Research Differ?

How are the approaches of quantitative and qualitative research different?

In qualitative studies, data is usually gathered in the field from smaller sample sizes, which means researchers might personally visit participants in their own homes or other environments. Once the research is completed, the researcher must evaluate and make sense of the data in its context, looking for trends or patterns from which new theories, concepts, narratives, or hypotheses can be generated.

Quantitative research is typically carried out via tools (such as questionnaires) instead of by people (such as a researcher asking interview questions). Another significant difference is that, in qualitative studies, researchers must interpret the data to build hypotheses. In a quantitative analysis, the researcher sets out to test a hypothesis.

Both qualitative and quantitative studies are subject to rigorous quality standards. However, the research techniques utilized in each type of study differ, as do the questions and issues they hope to address or resolve. In quantitative studies, researchers tend to follow more rigid structures to test the links or relationships between different variables, ideally based on a random sample. On the other hand, in a qualitative study, not only are the samples typically smaller and narrower (such as using convenience samples), the study’s design is generally more flexible and less structured to accommodate the open-ended nature of the research.

Below are a few examples of qualitative and quantitative research techniques to help illustrate these differences further.

Sources of Quantitative Research

Some example methods of quantitative research methods or sources include, but are not limited to, the following:

• Conducting polls, surveys, and experiments
• Compiling databases of records and information
• Observing the topic of the research, such as a specific reaction
• Performing a meta-analysis, which involves analyzing multiple prior studies in order to identify statistical trends or patterns
• Supplying online or paper questionnaires to participants

The following section will cover some examples of qualitative research methods for comparison, followed by an overview of mixed research methods that blend components of both approaches.

Sources of Qualitative Research

Researchers can use numerous qualitative methods to explore a topic or gain insight into an issue. Some sources of, or approaches to, qualitative research include the following examples:

• Conducting ethnographic studies, which are studies that seek to explore different phenomena through a cultural or group-specific lens
• Conducting focus groups
• Examining various types of records, including but not limited to diary entries, personal letters, official documents, medical or hospital records, photographs, video or audio recordings, and even minutes from meetings
• Holding one-on-one interviews
• Obtaining personal accounts and recollections of events or experiences

Examples of Research Questions Best Suited for Qualitative vs. Quantitative Methods

Qualitative research questions:.

• How do patients experience the process of recovering from surgery?
• Why do some employees feel more motivated in remote work environments?
• What are the cultural influences on dietary habits among teenagers?

Quantitative Research Questions:

• What is the average recovery time for patients after surgery?
• How does remote work impact employee productivity levels?
• What percentage of teenagers adhere to recommended dietary guidelines?

These examples illustrate how qualitative research delves into the depth and context of human experiences, while quantitative research focuses on measurable data and statistical analysis.

Mixed Methods Research

In addition to the purely qualitative and quantitative research methods outlined above, such as conducting focus groups or performing meta-analyses, it’s also possible to take a hybrid approach that merges qualitative and quantitative research aspects. According to an article published by LinkedIn , “Mixed methods research avoids many [of the] criticisms” that have historically been directed at qualitative and quantitative research, such as the former’s vulnerability to bias, by “canceling the effects of one methodology by including the other methodology.” In other words, this mixed approach provides the best of both worlds. “Mixed methods research also triangulates results that offer higher validity and reliability.”

If you’re enrolled as a National University student, you can watch a video introduction to mixed-method research by logging in with your student ID. Our resource library also covers qualitative and quantitative research methodologies and a video breakdown of when to use which approach.

When it comes to quantitative and qualitative research, methods of collecting data differ, as do the methods of organizing and analyzing it. So what are some best practices for analyzing qualitative and quantitative data sets, and how do they call for different approaches by researchers?

How to Analyze Qualitative Data

Below is a step-by-step overview of how to analyze qualitative data.

• Make sure all of your data is finished being compiled before you begin any analysis.
• Organize and connect your data for consistency using computer-assisted qualitative data analysis software (CAQDAS).
• Code your data, which can be partially automated using a feedback analytics platform.
• Start digging deep into analysis, potentially using augmented intelligence to get more accurate results.
• Report on your findings, ideally using engaging aids to help tell the story.

How to Analyze Quantitative Data

There are numerous approaches to analyzing quantitative data. Some examples include cross-tabulation, conjoint analysis, gap analysis, trend analysis, and SWOT analysis, which refers to Strengths, Weaknesses, Opportunities, and Threats.

Whichever system or systems you use, there are specific steps you should take to ensure that you’ve organized your data and analyzed it as accurately as possible. Here’s a brief four-step overview.

• Connect measurement scales to study variables, which helps ensure that your data will be organized in the appropriate order before you proceed.
• Link data with descriptive statistics, such as mean, median, mode, or frequency.
• Determine what measurement scale you’ll use for your analysis.
• Organize the data into tables and conduct an analysis using methods like cross-tabulation or Total Unduplicated Reach and Frequency (TURF) analysis.

Simply knowing the difference between quantitative and qualitative research isn’t enough — you also need an understanding of when each approach should be used and under what circumstances. For that, you’ll need to consider all of the comparisons we’ve made throughout this article and weigh some potential pros and cons of each methodology.

Pros and Cons of Qualitative Research

Qualitative research has numerous strengths, but the research methodology is only more appropriate for some projects or dissertations. Here are some strengths and weaknesses of qualitative research to help guide your decision:

• Pro — More flex room for creativity and interpretation of results
• Pro — Greater freedom to utilize different research techniques as the study evolves
• Con — Potentially more vulnerable to bias due to their subjective nature
• Con — Sample sizes tend to be smaller and non-randomized

Pros and Cons of Quantitative Research

Quantitative research also comes with drawbacks and benefits, depending on what information you aim to uncover. Here are a few pros and cons to consider when designing your study.

• Pro — Large, random samples help ensure that the broader population is more realistically reflected
• Pro — Specific, precise results can be easily communicated using numbers
• Con — Data can suffer from a lack of context or personal detail around participant answers
• Con — Numerous participants are needed, driving up costs while posing logistical challenges

If you dream of making a scientific breakthrough and contributing new knowledge that revolutionizes your field, you’ll need a strong foundation in research, from how it’s conducted and analyzed to a clear understanding of professional ethics and standards. By pursuing your degree at National University, you build stronger research skills and countless other in-demand job skills.

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While working on a research project, we often wonder whether our project is qualitative or quantitative in its approach. Although their objectives and applications overlap in many ways, there are significant differences between them. In this article, we’ll learn about Qualitative vs. Quantitative Research.

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What is Qualitative Research?

In qualitative research, different types of non-numerical data is gathered and evaluated to better understand ideas, views, or experiences (such as video, text, or audio). In-depth details about a situation can be discovered or ideas for fresh study concepts can be sparked through it. Quantitative research, which includes gathering and analyzing numerical data for statistical analysis, is the antithesis of qualitative research. The humanities and social sciences frequently employ qualitative research in sociology, anthropology, education, history, health sciences, etc.

Qualitative Data Analysis

Given that you have invested time and money in gathering your data, analysis of it is essential. You don't want to end up in the dark after making so much effort. Thus, it is a necessary step. There are no predetermined guidelines for assessing this material; the first step is comprehending its two basic methods.

Deductive Strategy

The deductive method entails examining qualitative data following a specified framework. The questions might serve as a roadmap for researchers as they analyze the data. When a researcher has a good sense of the expected replies he or she will obtain from the sample population, they can utilize this quick and simple method.

Inductive Method

Contrarily, the inductive method does not rely on preconceived guidelines or a predefined framework. It is a more extensive and time-consuming method of qualitative data analysis. Researchers frequently employ an inductive technique when they have little or no knowledge about the investigated phenomena.

Key Features Of Qualitative Research

• Content evaluation. Verbal or behavioral data must be categorized to classify, summarize, and tabulate.
• Analyzing narratives Utilizing the context of each case and the varied experiences of each respondent, this strategy entails reformulating the narrative that respondents have provided. In other words, narrative analysis is the researcher's reinterpretation of the original qualitative data.
• Analysis of discourse. A technique for analyzing all kinds of written material, including naturally occurring speech.
• Framework examination. This more sophisticated approach includes a number of steps, including familiarization, choosing a thematic framework, coding, charting, mapping, and interpretation.
• Solid theory. This approach to analyzing qualitative data begins with developing a hypothesis by examining a single example. 

Limitations of Qualitative Research

• The individual talents of the researcher are a major determinant of the research's quality, and the researcher's biases and quirks might have a greater impact.
• Rigor is more challenging to uphold, gauge, and prove.
• Analysis and interpretation take a lot of time because of the volume of data.
• Within the scientific community, it is occasionally not as well understood and accepted as quantitative research.
• The respondents' replies may be impacted by the researcher's presence, which is frequently unavoidable in qualitative research.
• Problems with confidentiality and anonymity might arise when disclosing findings
• Visually describing findings might be more time-consuming and complex.

Advantages Of Qualitative Research

1. understand the attitudes.

Consumer behavior is frequently malleable. Businesses may be left wondering what will happen to them if something happens unexpectedly. Qualitative research methods offer a plausible explanation for why a person's attitude could change.

2. It Generates Content

Even for a seasoned marketer, developing new methods to convey outdated material may be challenging. The qualitative research methodology enables the collection of real thoughts from certain socioeconomic demographics.

3. It Reduces Costs

Comparatively speaking to other research techniques, qualitative research employs a smaller sample size. This is a result of the fact that each participant is asked for more data. Less expensive research is associated with smaller sample sizes. This method of study not only saves money but it also has the potential to yield quicker findings. This is one of the greatest research methods now accessible if data is required rapidly for a crucial decision.

4. Offer Insights Unique To A Certain Sector

The two key elements for retaining customers are relationships and engagement. To communicate with their core demographics in a way that is as accurate and authentic as possible, modern organizations may employ qualitative research to uncover fresh insights that help advance these two essential elements.

5. Enables Creativity To Act As A Catalyst

Facts are frequently preferred above views in research. Instead of innovation, it wants observations. Unlike standard research, qualitative research follows a distinct path. Using this format, respondents won't seek to answer questions in a way that would suit the researcher, which tends to introduce bias into the collected data.

6. Ongoing, Open-Ended Process

Many people have a conditioned, skimpy response that they develop out of habit. Researchers can go further into these behaviors to uncover the real facts that a subject might offer by using the qualitative research technique. It has access to the emotional information that influences how we make decisions.

7. Takes Into Account Human Experience

Facts are crucial. Statistics can reveal patterns. The human experience, however, cannot be disregarded. Two people will each perceive the identical incident differently due to their unique human experiences. The intricacy of this material may be included in the findings drawn from the gathered study by conducting qualitative research.

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What is Quantitative Research?

The process of gathering and interpreting numerical data is known as quantitative research. In addition to identifying trends and averaging data, hypotheses can be formulated, causality can be examined, and findings can be extrapolated to greater populations. A comparative study, which gathers and examines non-numerical data, is known as quantitative research (e.g., text, video, or audio). The scientific and social sciences, including biology, chemistry, psychology, economics, sociology, and marketing, frequently employ quantitative research.

Key Features of Quantitative Research

The goal of descriptive research is to describe the current situation of a chosen variable. The purpose of these studies is to offer systematic data regarding phenomena. The researcher typically does not start with a hypothesis but is more likely to do so after gathering evidence. The hypothesis is tested through the analysis and synthesis of the data.

Using statistical data, correlational research aims to quantify the strength of a link between two or more variables. Relationships between and among various facts are looked for and understood in this design style. While this kind of study will spot trends and patterns in data, it does not go as far as to show the reasons behind the observed patterns.

The goal of causal-comparative/quasi-experimental research is to identify the causal links between the variables. Although there are some significant variations, these designs are extremely comparable to actual studies. The effects of an independent variable on the dependent variable are measured, but the investigator does not change the independent variable. The researcher must take advantage of naturally occurring or pre-existing groupings rather than create them randomly.

The scientific method, also known as real experimentation, is used in experimental research to determine the cause-and-effect link between the many study-related factors. The actual experiment is frequently viewed as a laboratory study, although this is not necessarily the case; the lab environment has no bearing on it.

Limitations of Quantitative Research

The fact that quantitative research techniques only provide a surface-level understanding of a phenomenon and ignore test-takers and testers' experiences as well as what they mean by certain terms is one of its limitations.

Advantages of Quantitative Research

1. can be examined and tested.

To do quantitative research, thorough experimental planning and the capacity for universal test and result replication are essential. As a result, the information you collect is more trustworthy and less subject to debate.

2. Clearly Stated Analysis 

The findings you get from collecting quantitative data can help you decide which statistical tests to run. As a result, your data interpretation and presentation of your findings will be simple and less vulnerable to mistakes and subjectivity.

3. Prestige

Many individuals don't comprehend the mathematics needed in such research; thus, it is valued and remarkable when it requires extensive statistics and data analysis. Technical innovations like computer modeling, stock picking, portfolio evaluation, and other data-driven business choices are connected to quantitative research.

Qualitative vs Quantitative Research - A Comparison

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When Do You Use Qualitative and Quantitative Research?

To understand qualitative research, let’s take the following example.

Qualitative Research Examples

Suppose a bookstore owner is looking for ways to improve their sales and customer outreach. An online community of readers who were the bookstore's loyal customers were interviewed, and related questions were asked, and they answered the questions. In the end, it was found that most of the books in the stores were for adults, and there were not sufficient books for children or teenagers.

By conducting this qualitative research, the bookstore owner realized what the shortcomings were and what were the feelings of the readers. Through this research, the bookstore owner can now keep books for different age groups and improve his sales and customer outreach. 

Qualitative Research Examples

Let's consider another example to understand quantitative research. Suppose any organization likes to conduct a customer satisfaction (CSAT) survey. For that, a customer satisfaction survey template can be implemented. Through this survey, a company can collect quantitative data and metrics on the goodwill of the brand or the company in the mind of the customer based on several parameters such as product quality, pricing, and customer experience. This data can be gathered by asking a net promoter score (NPS) question, and matrix table questions that provide data in the form of numbers that can be analyzed and worked upon using various analytics tools.

Now, let’s talk about Qualitative vs. Quantitative Research based on how data is collected for these research methods. 

Data Collection

Now, let’s talk about Qualitative vs. Quantitative Research based on the kind of research approaches they adopt.

Research Approach

For any research, sample data is important to derive meaningful information. Let’s understand Qualitative vs. Quantitative Research based on research samples.

Research Samples

With that, let’s now get an idea about the role of the researcher in qualitative and quantitative research.

Role of the Researcher

Now, let’s learn about Qualitative vs. Quantitative Research based on the scientific methods that are used in these techniques.

Scientific Method

Analyzing Data

Final report.

You may prefer to use only one type of research within a study, but the data generated from the research might not provide the desired results. To implement an unbiased research project that will provide accurate and meaningful insights, it is advised to consider both qualitative and quantitative research methods to get the right results. After reading this article, you would have learned the major differences between qualitative and quantitative research. 

If you want to learn more about different research techniques or how they impact your data and data analysis, then check out our extensive course on Data Analytics . Get in-depth with your analysis and jumpstart your career as a Data Analyst.

Do you have any questions related to Qualitative vs Quantitative Research? If so, then please put it in the comments section of this article. Our team will help you solve your queries at the earliest. 

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About the Author

Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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Qualitative and Quantitative Data Analysis Methods

Written by Ruchir Dahal on October 18, 2021

This is one piece of a three-part series that looks at the various methods, techniques , and essential steps to ensure superior data analysis.

The majority of leaders from high-performing businesses attribute their success to data analytics. According to a survey done by McKinsey & Company , respondents from these companies are three times more likely to pin their accomplishments on data analytics. 

That being said, although 1.145 trillion MB of data is created every day, stats show only 0.5% of it is analyzed to get results. This highlights a huge gap between companies that use data analytics to get ahead and those that don’t.

Grepsr has helped businesses bridge this gap, which in turn has given them an upper hand over their competitors. If you haven’t swiveled to data analytics yet, there is no better time to get started.

In the last post , we talked about the ‘why’ of data analysis, this time we will delve into the ‘how’. 

Qualitative and quantitative data

The internet has enabled us to create large volumes of data at a staggering pace. Moreover, the way you analyze it depends on the type of data you are working with. We broadly classify data into two forms — qualitative and quantitative .

Methods to data analysis

With so much data being created every day, it becomes imperative to go beyond the traditional methods to analyze this huge chunk of invaluable information.  

Qualitative and quantitative data each have their own ways of being processed. 

• Qualitative data analysis comes into play when the data you are trying to process has no bearing whatsoever with numbers, and cannot be tabulated — e.g. text, videos, images, etc.
• Quantitative data analysis deals with structured datasets that have numbers in them. It runs algorithms on statistical data to deduce objective truths.

Learn the key differences between qualitative and quantitative research from our recent blog.

Quantitative data analysis

Quantitative data analysis is a more traditional form of analysis. As mentioned earlier, this process crunches numbers to get results.  

Since one of the major functions of this process is to run algorithms on statistical data to obtain the outcome, the methods used in quantitative data analytics range from basic calculations like mean, median, and mode to more advanced deductions such as correlations and regressions.

Some of the scopes of quantitative data analysis include: 

• Project management
• Research and Development
• Product planning

Qualitative data analysis

Qualitative data analysis is used when the data you are trying to process cannot be adjusted in rows and columns. It involves the identification, examination, and elucidation of themes and patterns in data (mostly textual) to bolster the decision-making process.

Unlike quantitative analysis, qualitative data analysis is subjective . This method of analysis allows us to move beyond the quantitative traits of data and explore new avenues to make informed decisions.

The following are some of the scopes of qualitative data analysis: 

• Measuring customer satisfaction
• Monitoring competition
• Analyzing customer behavior
• Evaluating market trends

Learn more about qualitative data analysis in detail:

Differences between qualitative and quantitative data analysis

A veritable combination: qualitative and quantitative data analysis

Quantitative and qualitative data analysis when used together can help you generate deeper insights . More often than not, quantitative and qualitative data can be collected from the same data unit as you can see below.

To gain richer insights you can even pair these two methods in different domains. Best use cases include Google analytics & user interviews, social media & community engagement, marketing & surveys, and so on.

Bear in mind that your way of analysis completely depends on your requirements. Sometimes, quantitative analysis will be more than enough, and other times, only qualitative will do just fine. When you want to dig deep into the data at hand, it is advisable that you go with both qualitative and quantitative data analysis.

If you want to learn more about the different techniques to perform qualitative and quantitative data analysis, click here . To add to that, if you ever need to analyze large amounts of data or need expert help, you can hire the top freelance data analysts to augment your development teams for data analysis.

About Grepsr

We understand that your business has very specific needs when it comes to data. Be it data in the form of hard numbers or just images, we specialize in extracting data from far-flung areas of the internet.

Let us know about the data you need for effective analysis, and we will get back to you in a jiffy! 

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A user’s guide to data analysis.

29 min read Data on its own is nothing but facts and figures. To be useful, raw data needs to be broken down, modelled, and interrogated to provide useful information. You'll discover how to do this and more in this complete guide.

Data on its own is nothing but facts and figures. To be useful, raw data needs to be broken down, modelled and interrogated to provide useful information. Especially in businesses and organisations where decisions are based on data, collected data must be analysed and presented correctly and simply.

But what is data analysis, what are the best data analysis techniques, and why is it useful? Read on to find out.

What is data analysis?

Data analysis is a broad term that encompasses structured and scientific data collection, analysis, cleansing and data modelling. Data analysis applies to any source or amount of data, and helps to uncover insights and information that supports decision-making. Data science, on the other hand, only applies to quantitative data.

Data analysis typically revolves around two types of data: qualitative and quantitative data:

Qualitative data

Qualitative data is descriptive and typically unstructured. Examples of qualitative data include: interview transcripts, open-ended answers to surveys, field notes, recordings, questionnaires (but only if you have a small sample size) and so on.

Quantitative data

Put simply, quantitative data is survey data with sufficient sample sizes. It’s essentially data that you can count and assign a numerical value, e.g. revenue in dollars, weight in kilograms, height in feet or inches, length in centimetres. Quantitative data is often structured and suitable for statistical analysis .

You can use both types of data to gain an understanding of the entire business landscape, from how your offering fits into the picture to what customers are looking for.

For example, it can be used to understand the marketplace, your business’ position within the marketplace, and provide customer insights around preferences, decisions and behaviours.

We’ve added a table below to provide you with an at-a-glance view of the differences between qualitative and quantitative research.

Find out more about qualitative and quantitative research

Why is data analysis important?

The use of data in decision-making has become more important than ever. Across the globe, businesses and organisations are basing decisions on facts and proven theories, supported by data analysis, rather than making critical decisions on the best guess. These approaches are helping them to plan more efficiently, sustain growth and deliver amazing products.

Here are a few examples of how you can use data analysis:

Customer insights

Using the right data analysis methods, you can gain a complete understanding of your customers.

You can learn everything from their brand, product and service preferences to which channels they use when researching those products and services. You can even uncover their shopping behaviours and how they change based on what they’re buying.

You can also use data analysis to better understand your customers’ demographics, interests and habits so you can better tailor your marketing and brand messaging around themes they’ll connect with.

Marketing insights

Marketing is highly reliant on information and data analysis to measure its effectiveness and success — or failure.

From website analytics like measuring traffic and leads to analysing brand sentiment, data analysis is critical to understanding the full picture of your marketing strategy. Based on measurement requirements, you can implement data collection and analysis methods to plug the gaps across the entire buyer journey, enabling you to make specific changes at each stage to help drive growth.

Presentation techniques like data visualisation also form a huge part of marketing analysis (we’ll talk more about data visualisation later on).

Market insights

Understanding the marketplace is an essential part of figuring out your positioning, how the market is changing and what your business needs to do to adapt to competitors or fast-paced market conditions.

All this can only be understood with the constant collection and analysis of data.

By collecting accurate data, analysing it closely and gaining insights you can ensure your organisation is ready and able to adapt when it needs to.

Data analysis methods

It’s important to understand that there are many different types of data analysis. Each type has its uses, and your choice ultimately depends on what kind of information you want to get out of the process — and if you want to get qualitative or quantitative data.

Here’s a quick breakdown of some the main types of data analysis you can use and what they’re best for:

Descriptive analysis

Descriptive analysis is a form of data analysis that uses responses from a data set to establish patterns or understand the average of one variable (both in isolation and how it compares to other variables in the study).

Descriptive analysis is typically used as a means of understanding occurrences or preferences. For example, you would use descriptive analysis when trying to determine the most popular type of exercise people did in the last year.

In your survey, you’d present a range of responses (running, weight lifting, swimming). Once the survey results were collected, you could use descriptive analysis to understand the popularity of each activity, the averages among the variables and how each variable compares to the other.

Diagnostic analysis

Where descriptive analysis is about understanding trends, diagnostic analysis is the next stage in uncovering the causes of those trends and highlighting any correlation between the variables in your study.

Diagnostic analysis usually starts with a hypothesis that you’re trying to prove or disprove. For example, your hypothesis could be that the number of people signing up to a gym in August is caused by the hot weather.

You’ll use this hypothesis to guide your data analysis and keep you focused on what you’re looking at.

Another thing to keep in mind with diagnostic analysis is understanding the distinction between correlation and causation.

Misunderstanding these two concepts could lead to you making incorrect interpretations of your data. When we talk about correlation, there can are two types you likely see:

1. Positive correlation

Let’s say we have two variables. A positive correlation means that as one variable increases, so does the other.

2. Negative correlation

In this case, as one variable increases, the other decreases.

However, just because the variables are correlated, it doesn’t always mean that one change is caused by the other.

When doing diagnostic analysis, causation is the ideal insight to gain, but correlation can still provide useful insights too.

3. Causation

In research, causation is when one factor (or variable) causes another. In other words, there is a cause-and-effect relationship between the two. Correlation doesn’t imply causation, but causation always implies correlation.

For example, a lack of graphics card memory can cause visual processes on a computer to freeze or not work entirely. Visual process failures and an inoperable graphics card are correlated, but they don’t highlight the cause. Similarly, a lack of random access memory (RAM) might cause your phone to freeze or lock up — the lack of RAM is the cause and the two issues (the phone freezing and locking up) are correlated, but neither causes the other.

Causation is important because it brings you to the root of all issues and enables you to see how other variables are influenced in the process.

Exploratory analysis

Exploratory analysis is used by data analysts to assess raw data and understand its primary characteristics, and then determine how to manipulate the data sources to get different answers.

Exploratory analysis is primarily used to prove the validity of results gathered from data and that they apply to any goals or objectives. Essentially it’s used as a way to use data before making any assumptions about a situation.

Once the raw data is collected, data analysts can then manipulate the data sources to test the impact of changing variables on the overall results. It can be particularly useful when checking assumptions, uncovering anomalies and discovering patterns.

Exploratory analysis can also be used for more complex studies and modelling, like machine learning.

Predictive analysis

As the name suggests, predictive analysis is a data analysis process that uses historical data, algorithms and even machine learning to try to predict what will happen in the future based on previous trends.

Predictive analysis has been rapidly growing in popularity in businesses and organisations as the data analysis tools used to map the future predictions – and better advances in machine learning – have made predictions more accurate.

It also has multiple business applications, which is another reason it’s so popular.

For example, predictive analysis is becoming a key part of cyber security and crime prevention — particularly when it comes to fraud.

By compiling vast amounts of data in real-time and analysing results and trends to uncover patterns, predictive analysis can help data analysts detect abnormalities in behaviour or actions based on previous events and take the necessary actions.

Reducing consumer risk is another major area for predictive analytics and it’s often used when assessing credit scores and predicting whether customers can afford to take on expensive financial products – like a mortgage or loan – based on their previous history.

Leisure businesses like airlines and hotels also use predictive analytics by using data from previous years to predict usage when setting prices.

Find out more about predictive analytics

Prescriptive analysis

Prescriptive analysis is one of the more advanced data analysis techniques and is often used by businesses and organisations trying to work out what decisions to make, or what steps they need to take to make something happen, such as increasing sales in Q4 compared to the previous year.

It involves data mining from multiple sources like resources, historical data and past performances and using advanced analysis techniques like machine learning to model the data and provide insights to guide future decisions.

It’s a relatively new type of data analysis because it relies on the collection and analysis of huge amounts of data and advanced technology like artificial intelligence or machines to process and analyse the data effectively.

As more data is provided and analysed, the models will become more accurate and change based on the variables being input.

Prescriptive analysis is often accompanied by data visualisation tools to help decision-makers understand what they’re looking at and take appropriate actions.

It does, however, require businesses and organisations to know the types of questions to ask to gain the correct information and stop people from making decisions based on the wrong assumptions. It also requires the ability to make the right judgments based on the data models provided.

The data analysis process

The data analysis process is a fairly straightforward one regardless of the data analysis techniques being used.

But you do need to follow it properly if you want to capture the right data and glean meaningful insights that you can use.

You can see the outline of the data analysis process in the graphic above, but this is a quick rundown of the data analysis steps you’ll take:

1.   What’s the goal of the objective?

The first question to ask before embarking on any data analysis is why are you looking to analyse data in the first place?

Do you need it to guide strategic business decisions or update business processes? Are you trying to find an answer to a specific question or do you want to test a hypothesis? Or do you need to make improvements to operational processes but don’t know where to start?

Why you need data can help you figure out the right data analysis methods to choose and also guide key decisions like whether you need qualitative data analysis or quantitative data analysis (or a combination of the two).

Then there’s the what of your data analysis — what data should you be collecting? What conclusions do you want to draw from the data? Clearly defining the what will help you to select the appropriate tools and methodology to capture the data for the why.

2.   Collecting raw data

Once you’ve decided why you need the data and what method of data analysis is best, you can start the process of collecting your raw data.

This could be devising and sending out surveys for quantitative data analysis, emailing customers questionnaires or running focus groups for qualitative data analysis.

3.   Data cleaning

Regardless of how you collect your data, you have to account for errors and put measures in place to prevent them. For example, preventing participants from submitting the same survey twice to avoid duplication. Instead, allow them to modify their answers (if it’s an online survey).

For incomplete surveys, questionnaires or interviews, ensure that participants can only submit them once they’ve filled in the required fields. If they don’t have an answer, encourage them to put N/A, for instance.

Incorrect, incomplete and duplicate responses can affect the quality, consistency and usability of your data, preventing you from making accurately informed decisions.

4.   Begin analysing data

Once you’ve collected and cleansed your data, you can begin the process of data analysis. As you’re analysing your data you’ll be able to identify patterns in your data that you can use to inform your business decisions.

The most common data analysis techniques

(Predictive) Regression analysis : used to estimate the relationship between a set of variables. The idea is to find a correlation between a dependent variable (the outcome you want to measure or predict) and any number of independent variables.

For example, if you’ve been planting trees in an area at the same rate every day for a year, it can predict how many trees you’ll have planted in 5 or 10 year’s time.

(Predictive) Time series analysis: Time series analysis is a statistical technique used to identify trends and cycles over time, e.g. sales forecasts such as weekly or monthly profits, or fluctuations based on seasonality.

For example, time series analysis is helpful when it comes to industry forecasts, weather data and rainfall measurements, and stock prices. This is because rather than collecting data over time, analysts record data points at specific intervals, giving a complete, constantly evolving picture of the data.

(Predictive and prescriptive) Monte Carlo simulation: this is a complex, computerised technique designed to generate models of possible outcomes and their probability distributions. It measures a range of possibilities and calculates their likelihood.

This simulation is used for outcomes that are difficult to predict due to the intervention of random variables. It helps to understand the impact of risk and uncertainty in predictions and forecasting models. It’s also referred to as a multiple probability simulation.

For example, you could use Monte Carlo simulation to determine which moves to play in chess (of which there are between 10 111 and 10 123 positions (including illegal moves), which is more than the number of atoms in the world. The computer essentially calculates

all these possible moves (with the most powerful computers doing trillions of calculations per second) and continuously plays until it satisfies a set end condition, e.g. a win.

(Exploratory) Factor analysis : Factor analysis is used to reduce a large number of variables to a smaller number of impactful factors. It condenses large datasets into smaller, manageable chunks and helps to uncover hidden patterns.

For example, say you conduct a survey of 500 townspeople, resulting in a dataset of 500 variables. You could work to find which variables are correlated and put them into groups, e.g. income, education, family size. These groups are factors. From there, it becomes easier to analyse the data.

(Exploratory) Cohort analysis: Cohort analysis is a subset of behavioural analytics that takes the data from a given dataset and breaks it into groups for analysis. These related groups (or cohorts) usually share common characteristics or experiences.

For example, you could use cohort analysis to understand customer expenditure over time. You can analyse trends in purchase behaviour and then gauge whether or not the quality of the average customer is increasing throughout the lifecycle.

(Exploratory) Cluster analysis : This exploratory technique seeks to identify structures and patterns within a data set. It sorts data points into groups (or clusters) that are internally similar and externally dissimilar.

For example, in medicine and healthcare, you can use cluster analysis to identify groups of patients with similar symptoms. From there, you can alter your method of care. You can also use cluster analysis in areas like marketing to identify homogeneous groups of customers that have similar needs and attitudes.

(Exploratory) Sentiment analysis : A qualitative technique that belongs to a broad category of text analysis. Sentiment analysis looks at, assesses and classifies emotions conveyed through text data.

For example, you can use it to determine how customers feel about your brand, product or service based on feedback.

5.   Data interpretation

Once you’ve finished analysing data from your study, you can begin your data interpretation and begin to apply actions based on what the results are telling you.

There are plenty of business intelligence tools you can use to model your data to make this interpretation easier and ensure you can make decisions quickly. We’ll outline a few of those tools shortly — but first, here are a few mistakes to avoid.

Common mistakes in data analysis

Diligence is essential when it comes to data analysis — but when you’re running complex studies at scale, it’s sometimes hard to keep on top of the quality and assurance process. Here are just a few of the most common data analysis mistakes researchers make and how to avoid them:

Sample bias

Sample bias is when you choose a sample that is non-representative of the wider population you’re trying to assess.

Any bias in your sample can result in data skewing more to one side and not providing reliable results.

A simple example of this is sampling 1,000 people to assess political preferences but oversampling one political allegiance.

One of the most effective ways to avoid sampling bias is to use simple random sampling. This ensures that samples are chosen by chance — and every person in the population has an equal chance of being selected.

Intentional data manipulation

This happens in data science when data analysts try to fit their data to support a particular theory or hypothesis. It can occur by accident, but is typically an intentional act and can have a serious impact on the validity of the study. Data manipulation also applies when participants can submit a survey more than once, skewing the overall results if you don’t double-check duplicate contact data.

You can avoid data manipulation by:

• Ensuring participants use contact information that can be verified and cannot submit using those same details twice (but give them the option to amend their responses)
• Using a system designed for market research and capable of highlighting duplicate submissions/errors
• Using multiple contact methods — so if you’re collecting data at point of service or contact, ensure you run an online element as well. For example, you could ask customers to take a survey in store (or as they’re leaving) and then email other customers or engage with them via social media channels.
• Allowing customers to remain anonymous
• Vetting and assessing surveys and questionnaires before they’re submitted to participants to prevent any bias or leading questions

Disclosing your study’s metrics

The respondents of your study should never be aware of the metrics you’re measuring your study with, because once they do you could end up in a situation where they try to tell you what you want to know.

That said, it’s helpful to provide respondents with guidance and the context of the study — why it’s important and relevant and how their honest responses can contribute to its validity. This includes a preface about the questions that are going to be asked.

Fitting the data to your model

This is a very common problem in data science when you find that data only shows you a result because it fits the data modelling you’re using it with. If you move the data over to another model, you could find it doesn’t then show any results.

For example, you might focus so heavily on the accuracy of a particular model that it can only fit a particular use case, e.g. measuring sales trends based on seasonality. Analysts typically build and use machine learning models to apply them to general scenarios — not incredibly specific ones. Overfitting the model will mean that it only works for one situation and subsequently fails others.

This particular problem is like comparing apples with oranges. If you create a time series model specifically for seasonal sales trends, that model will only give you that data. You can’t suddenly apply the data to other models and expect to get the same results, because they won’t account for the same variables.

Focusing only on quantitative data

Numbers can only tell you part of the story, and making decisions on numbers alone can result in negative consequences.

While quantitative data can produce some useful insights it must also be used in the context of the wider market or environment and, ideally, aided with some qualitative insights.

Having solution bias to your products

Solution bias can be a big risk for businesses who are convinced they have a good product or service, and are trying to find data to support their theory at any cost.

In these circumstances, you could end up with data analysis that you’re using simply to confirm your own assumptions, rather than properly testing your theory.

What data analysis tools can you use?

There are several tools available that can make analysing raw data easier and improve your data visualisation so you can easily interpret your information and make decisions.

Here are some of the most common and best data analysis tools available:

R is a free, open-source data analysis tool that can be used for a range of data science, statistical analysis and data visualisation studies.

Using R, data analysts can set up and run data models, which can be automatically updated as new information is added.

SAS is one of the most widely used software packages for data analysis. It’s primarily used for statistical analysis and data visualisation.

As well as quantitative analysis, SAS can be used for qualitative analysis, is highly customisable based on the data analysis you want to use, and offers a range of statistical methods and algorithms.

Python is an effective tool for every stage of data analytics and is widely used by data analysts. It’s an easy software language to learn and use and is highly effective for data mining — particularly when scraping large amounts of data from multiple sources.

Java is one of the most versatile statistical programming languages as well as coding languages that can be used for numerous types of data analysis. Java can be connected to SQL and MySQL databases. It’s also highly customisable and can be used for mass data scraping, analysis, and visualisation.

SQL is a relatively simple processing language that can be used to interact with multiple data sources at once, making it highly effective. It can also perform complex data analysis which, when combined with the data sources used, make it a highly accessible and effective data analysis tool for data analysts.

Using data analysis to improve your ROI

Data analytics is a highly effective tool for improving ROI because you can be sure you’re making decisions based on data, rather than instinct.

This is particularly true when it comes to using data analytics to gain customer insights, but also for improving specific aspects of your business. For example, you could carry out an employee engagement survey and use data analytics to uncover trends and areas for improvement. Your analysis of the survey results might find that your employees want more remote working/hybrid working options — you can then implement new flexible and hybrid working policies to support them. This in turn will improve productivity and engagement, which in turn can support your bottom line.

Here’s another example — let’s say you run a conjoint analysis survey to determine the optimal bundle of benefits and features, including price. You can readily test product options, features and pricing to find out what customers are most likely to buy and therefore what product variants will contribute to your bottom line.

You can even apply data analytics to product concept testing to uncover whether or not your product ideas are up to scratch and marketable to your target audience. This ensures you spend less time investing in ideas that won’t work, and more time on ideas that will.

The possibilities are endless and data analysis applies to every area of your business. By taking the time to analyse trends within your data, you can start to create better experiences and outcomes for all.

Free eBook: Start maximising your research ROI

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Market intelligence tools 10 min read, qualitative research questions 11 min read.

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Preparing to Write Survey Questions

Choosing your data type, qualitative.

Data that is verbally based (words and concepts).

Quantitative

Data is numerically based (numbers only).

One survey data type is not necessarily better than another. As summarized by Ahmad (2019), “Quantitative data can help to see the big picture. Qualitative data adds the details.” What type of data you need is going to be dependent on what you are trying to analyze.

Choosing Your Question Format

Open-ended questions.

Open-ended questions are those in which a survey respondent can generate a unique response using their own words. These are seen predominantly in qualitative data surveys. These types of questions are particularly useful when information is needed about individual-specific context that might not be accounted for in a multiple-choice type closed-ended format. A key benefit of open-ended questions is that they allow for respondents to give personalized responses that are not confined to the choice selection set by the researcher. This is simultaneously a key disadvantage, however as it introduces the need for qualitative coding and in turn, introduces a new source of error.

Some examples of open-ended survey questions would be:

• What was your impression about working in groups?

Describe any study techniques you found to be beneficial.

Open-ended survey questions are generally useful for small sample populations. Though these questions offer great insights, on a large sample population scale they are often unfeasible due to the administrative planning and analysis required. If you want the insight from open-ended questions but are working with a large respondent pool, a small sample population can be given an open-ended question-based Pilot Survey to obtain information and inform the design of close-ended question surveys for your larger populations.

Closed-Ended Questions

Closed-ended questions are those in which the response options are limited and provided with the survey. These can be used in qualitative data acquisition as well as quantitative. Some of the main benefits of closed-ended questions are the reduction of the need for communication skills on the behalf of the respondent and the ease of analysis. Conversely, some of the main disadvantages include lack of depth in responses and lack of emergent insights (Hyman and Sierra 2016). These question types help to eliminate sources of error in the data analysis by reducing or eliminating the need to code free response answers; however, the data obtained will be limited to response options generated by the researcher. Closed-ended survey questions can often be evaluated statistically and are easier to use when evaluating large sample sizes, which encourages studies that are more generalizable. Some examples of closed-ended survey questions would be:

• Rewriting Notes
• Group Study
• None of these

Thinking Ahead

Often, qualitative data are linked to open-ended questions while closed-ended questions are paired with quantitative data. The reality is that open-ended qualitative questions can be converted into quantitative data and conversely closed-ended quantitative questions can be used to glean qualitative data. The key is the coding, so write the questions in whichever way will give you the data you are most needing to see while keeping in mind the logistical elements that come along with delivering each type to the population you are studying.

Ahmad, S., Wasim, S., Irfan, S., Gogoi, S., Srivastava, A., & Farheen, Z. (2019). Qualitative v/s. Quantitative Research- A Summarized Review. Journal of Evidence Based Medicine and Healthcare, 6(43).  https://journals.indexcopernicus.com/api/file/viewByFileId/916903.pdf

Hyman, M., & Sierra, J. (2016). Open- versus close-ended survey questions.  NMSU Business Outlook ,  14 (2), 1–5.

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This paper is in the following e-collection/theme issue:

Published on 9.7.2024 in Vol 26 (2024)

Determining an Appropriate Sample Size for Qualitative Interviews to Achieve True and Near Code Saturation: Secondary Analysis of Data

Authors of this article:

Original Paper

• Claudia M Squire, MS   ; 
• Kristen C Giombi, PhD   ; 
• Douglas J Rupert, MPH   ; 
• Jacqueline Amoozegar, MSPH   ; 
• Peyton Williams, MPH  

RTI International, Research Triangle Park, NC, United States

Corresponding Author:

Claudia M Squire, MS

RTI International

3040 East Cornwallis Road

Research Triangle Park, NC, 27709-2194

United States

Phone: 1 9195416613

Email: [email protected]

Background: In-depth interviews are a common method of qualitative data collection, providing rich data on individuals’ perceptions and behaviors that would be challenging to collect with quantitative methods. Researchers typically need to decide on sample size a priori. Although studies have assessed when saturation has been achieved, there is no agreement on the minimum number of interviews needed to achieve saturation. To date, most research on saturation has been based on in-person data collection. During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was possible. Researchers continue to use web-based data collection methods post the COVID-19 emergency, making it important to assess whether findings around saturation differ for in-person versus web-based interviews.

Objective: We aimed to identify the number of web-based interviews needed to achieve true code saturation or near code saturation.

Methods: The analyses for this study were based on data from 5 Food and Drug Administration–funded studies conducted through web-based platforms with patients with underlying medical conditions or with health care providers who provide primary or specialty care to patients. We extracted code- and interview-specific data and examined the data summaries to determine when true saturation or near saturation was reached.

Results: The sample size used in the 5 studies ranged from 30 to 70 interviews. True saturation was reached after 91% to 100% (n=30-67) of planned interviews, whereas near saturation was reached after 33% to 60% (n=15-23) of planned interviews. Studies that relied heavily on deductive coding and studies that had a more structured interview guide reached both true saturation and near saturation sooner. We also examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes represented previously established core concepts or themes. Codes representing newly identified concepts, other or miscellaneous responses (eg, “in general”), uncertainty or confusion (eg, “don’t know”), or categorization for analysis (eg, correct as compared with incorrect) were less commonly applied after near saturation had been reached.

Conclusions: This study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population under study. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites.

Introduction

In-depth interviews are commonly used to collect qualitative data for a wide variety of research purposes across many subject matter areas. These types of interviews are an ideal approach for examining individuals’ perceptions and behaviors at a level of depth, complexity, and richness that would be challenging to achieve with quantitative data collection methods. Typically, trained interviewers conduct interviews using a guide designed to address the study’s key research aims by asking a series of questions and probes ordered by topic. These interview guides can range from highly structured to completely unstructured (eg, loosely organized conversations). Following the completion of data collection, interview notes and transcripts generated from audio recordings of the interviews are analyzed to assess for patterns in responses among the interviewees or subsets of the participants [ 1 , 2 ].

During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was not possible, and researchers continue to use web-based data collection methods post the COVID-19 emergency, citing advantages such as accessing marginalized populations, achieving greater geographic diversity, being able to offer a more flexible schedule, and saving on travel expenses [ 3 ]. Potential concerns about web-based data collection, such as the inability to build rapport and data richness, have been largely unfounded [ 3 , 4 ].

While we do not expect web-based data collection to supplant in-person research, it continues to show signs of growth. To date, much of the research on qualitative methods has focused on in-person data collection. Consequently, it will be important to conduct research to determine if previous widely accepted findings hold true for web-based data collection.

Researchers typically make a priori decisions about the number of interviews to conduct with the aim of balancing the need for sufficient data with resource limitations and respondent burden. The concept of saturation is frequently used to justify the study’s rigor with respect to the selected sample size. To provide empirically based recommendations on adequate minimum sample sizes, researchers have conducted studies to assess when saturation occurs. However, multiple types of saturation exist—such as theoretical, thematic, code, and meaning—and within each type of saturation, the definitions and measurement approaches used by investigators vary substantially, as does the level of detail researchers report in publications about their methods for achieving and assessing saturation [ 5 ].

This study aimed to examine the number of interviews needed to obtain code saturation for 5 recently conducted studies funded by the Food and Drug Administration [ 6 ] involving web-based interviews. Specifically, how many web-based interviews are needed to obtain true code saturation (ie, the use of 100% of all codes applied in the study) and how many web-based interviews are needed to achieve near code saturation (ie, the use of 90% of all codes applied in the study)?

Literature Review

Multiple authors have defined saturation as the point during data collection and analysis, at which no new additional data are found that reveal a new conceptual category [ 7 - 13 ] or theme related to the research question—an indicator that further data collection is redundant [ 11 ]. Additionally, Coenen et al [ 14 ] specified that no new second-level themes are revealed in 2 consecutive focus groups or interviews.

Other authors have distinguished between various types of saturation. One of the most common types of saturation mentioned in the literature is theoretical saturation, which emerges from grounded theory and occurs when the concepts of a theory are fully reflected in the data and no new insights, themes, or issues are identified from the data [ 5 , 11 , 12 , 15 - 18 ]. Hennink et al [ 17 ] expanded this definition, adding that all relevant conceptual categories should have been identified, thus emphasizing the importance of sample adequacy over sample size. Guest et al [ 15 ] operationalized the concept of theoretical saturation as the point in data collection and analysis when new information produces little or no change to the codebook, and van Rijnsoever [ 19 ] operationalized it as being when all the codes have been observed once in the sample.

Some authors have defined theoretical saturation, thematic saturation, and data saturation as the same concept [ 16 , 18 ], whereas others have defined these terms differently [ 12 , 20 ]. For example, some authors have defined thematic saturation as the point where no new codes or themes are emerging from the data [ 12 , 21 ]. For thematic saturation to be achieved, data should be collected until nothing new is generated [ 20 , 22 ]. Data saturation has been defined as the level to which new data are repetitive of the data that have been collected [ 12 , 23 , 24 ].

Furthermore, Hennink et al [ 17 ] distinguished between code saturation and meaning saturation. Code saturation is based on primary or parent codes and relates to the quantity of the data (“hearing it all”). Meaning saturation is based on sub or child codes and relates to the quality or richness of the data (“understanding it all”). Constantinou et al [ 7 ] made the point that it is the categorization of the raw data, rather than the data, that are saturated.

The literature reflects multiple methods that have been used to determine saturation [ 7 - 10 , 13 - 18 , 21 , 25 ]. Sim et al [ 26 ] discussed the four general approaches that have been used to determine sample size for qualitative research: (1) rules of thumb, based on a combination of methodological considerations and past experience; (2) conceptual models, based on specific characteristics of the proposed study; (3) numerical guidelines derived from the empirical investigation; and (4) statistical approaches, based on the probability of obtaining a sufficient sample size.

For example, Galvin [ 9 ] used a statistical approach based on binomial logic to establish the relationship between identifying a theme in a particular sample and within the larger population; for example, number of chances of detecting a theme if that theme exists within number of the population. Using the probability equation, the researcher can determine the number of interviews needed for a stated level of confidence that all relevant themes held by a certain proportion of the population will occur within the interview sample. This method assumes the researcher knows in advance the emergent themes from the study and at what rate they may occur.

Constantinou et al [ 7 ] used the comparative method for themes saturation, which relies on both a deductive and an inductive approach to generate codes (keywords extracted from the participants’ words) and themes (codes that fall into similar categories). Themes are compared across interviews, and theme saturation is reached when the next interview does not produce any new themes. The sequence of interviews is reordered multiple times to check for order-induced error. When exploring the various methods for determining saturation, researchers reached different conclusions on when saturation was achieved (findings on saturation by other authors are present in Multimedia Appendix 1 ) [ 7 - 10 , 13 - 17 , 21 , 25 , 27 , 28 ].

Most studies assessing saturation focused on in-person data collection or did not specify the data collection method. Given recent increases in web-based data collection, studies assessing saturation for web-based interviews are critical to ensure that recommendations regarding sample size are tailored to the mode of data collection [ 4 ]. While there is evidence to suggest that the content of data coded from in-person as compared with web-based interviews is conceptually similar [ 29 ], this is a relatively new area of exploration. Rapport may be higher with in-person as compared with web-based interviews [ 30 ], which may impact the amount and type of content generated. Additionally, participants in web-based data collection studies are more geographically diverse and may be more likely to be non-White, less educated, and less healthy than participants in in-person data collection studies [ 31 ].

Study Design

This study was based on analyses from data collected for 5 Food and Drug Administration–funded studies conducted using web-based platforms, such as Zoom (Zoom Video Communications) and Adobe Connect (Adobe Systems), and focused on patients with underlying medical conditions or on health care providers who provide primary or specialty care to patients. All platforms used for these interviews offered audio and video components and allowed for the sharing of stimuli on screen. A brief description of each study is provided in Table 1 . Each study’s data had been coded and stored using NVivo software (version 11; QSR International).

Ethical Considerations

This project was determined to not research with human participants by Research Triangle Institute’s institutional review board (STUDY00021985). The original 5 studies that this project is based on were reviewed by Research Triangle Institute’s institutional review board and were determined to be exempt under category 2ii. Participants in these studies were provided information about measures used to protect their privacy and the confidentiality of their data in the study’s consent forms. All participants were provided compensation for their time (the amount and type varied by study).

Data Preparation and Analysis

We established and applied a systematic approach to analyze all 5 study data sets. Our analytic approach was organized into 2 stages—data preparation and data analysis.

Data Preparation

First, because previous interviews sometimes influence moderator probes—for example, the moderator asks a follow-up question based on something they heard in a previous interview—we sorted interviews from each study by interview order. We then extracted code- and interview-specific data from the NVivo databases—including transcript name, code name, number of files coded, number of associated parent and child codes, and number of coding references—and compiled these data in an Excel (Microsoft Corp) file. We then updated the Excel file with important code and interview characteristics, including the order in which interviews were conducted, whether each code was directly (ie, child codes) or indirectly (ie, parent codes) applied to transcripts (in a tiered coding scheme, direct codes are those that have no child codes, whereas indirect codes function as “parents” that have additional codes nested beneath them), and the point at which each code was first applied to an interview. Finally, we created pivot tables within each Excel file to compile the data.

Data Analysis

Once the data were compiled, the data summaries were examined to determine when true saturation and near saturation occurred during data collection. True saturation was defined as 100% of all applied codes being used; near saturation was defined as 90% of all applied codes being used. We calculated saturation separately for each study’s data set, and we calculated saturation separately for all codes (ie, parent and child codes) as compared with direct codes (ie, child codes only). True saturation and near saturation points were identified by calculating the cumulative percentage of new codes for each interview, flagging when 100% and 90% of applied codes had been used.

True and Near Saturation

The number of web-based interviews used across the 5 studies ranged from 30 to 70 ( Table 2 ). True saturation (100% use of all applied codes) was reached in the final or near final interview ( Figure 1 ), suggesting that, even with a large sample size, additional interviews are likely to continue uncovering a small number of new codes or findings.

Across all studies, near saturation (90% use of all applied codes) was reached near—and often before—the midpoint of data collection. In other words, only a small number of new codes or findings were uncovered once the first half of the sample had been interviewed. In terms of absolute numbers, the point at which near saturation was reached occurred between 33% and 60% (n=15-23) of planned interviews ( Table 2 ). Despite the participants being more geographically, and possibly demographically, diverse compared with typical in-person participants, our findings were similar to previous studies on saturation [ 10 , 15 , 17 ].

We examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes (n=8-33, 57%-62%) represented previously established core concepts or themes, such as a trusted source of information, a behavioral intention, or a recommended change to educational material. Codes representing newly identified concepts (n=2-8, 10%-15%), other miscellaneous responses (eg, “in general”; n=6-9, 13%-41%), uncertainty or confusion (eg, “don’t know”; n=0-6, 0%-11%), or categorization for analysis (eg, “correct as compared with incorrect”; n=0-3, 0%-4%) were less commonly applied after near saturation had been reached.

The overwhelming majority of codes applied after near saturation (n=9-41, 73%-82%) had already been established in study codebooks before analysis. Only a small number of codes applied after this point (n=4-20, 18%-27%) were conceptually distinct enough to merit updating the study codebooks by including them. Likewise, most of the codes used after near saturation (n=11-35, 44%-64%) were applied to only a single interview. Far fewer codes were applied to 2 interviews (n=0-13, 0%-27%), 3 interviews (n=0-6, 0%-21%), or 4 or more interviews (n=0-12, 0%-21%).

Study B was an outlier in terms of codes applied after near saturation. This study had fewer codes representing core established concepts (n=8, 28%) and more codes representing newly identified concepts (n=7, 24%) or providing categorization for analysis (n=3, 10%) than other studies. The study also had a much higher proportion of new codes (n=20, 69%) that were added to the study codebook during analysis. These differences may be because the study sampled 2 populations with very different medical conditions (ie, type 2 diabetes as compared with vulvovaginal atrophy), leading to a broader range of applied codes.

In examining the relationship between the number of codes in the codebook for each study, the study with the most codes (study A: 657 codes) required the largest number of interviews to reach both true saturation and near saturation. However, this pattern did not hold true for the remainder of the studies. The study with the next highest number of codes (study C: 362 codes) was third to reach true saturation and last to reach near saturation.

Parent and Child Codes

All 5 study codebooks included both parent (ie, top-level codes) and child codes (ie, subcodes). We examined saturation using two analytic lenses—(1) all codes (parent and child) and (2) parent codes only—to determine if there were differences in when saturation was reached. We found no differences in when true saturation was reached. However, near saturation was reached slightly later (ie, after an additional 3 to 4 interviews) when examining only parent codes ( Figure 2 ).

Differences by Study

In total, 3 of the studies had codebooks that consisted almost entirely of deductive (ie, concept-driven) codes, whereas the codebooks in the remaining 2 studies contained a mix of both deductive and inductive (ie, data-driven) codes. Although the results were largely consistent across the 5 studies, as expected, the studies that relied heavily on deductive coding reached both true saturation and near saturation sooner. This finding suggests that studies using more inductive coding and analytic techniques may require slightly larger sample sizes to reach saturation.

Structure of an Interview Guide

Although all the studies used a semistructured interview guide, the level of structure varied across studies. The 3 studies (ie, studies C, D, and E) that had a more structured interview guide (eg, questions for which participants were asked their preference among discrete choices or the range of likely answers was limited) reached both true saturation and near saturation sooner. In fact, the study with the most structured guide reached near saturation the soonest, although it fell in the middle for true saturation. This finding suggests that studies using a less structured interview guide may need to conduct more interviews to reach an acceptable level of saturation.

Principal Findings

Although true saturation was not reached until the final interview or close to the final interview, near saturation was reached much sooner, ranging from just below to just above the midpoint of data collection, with most of the studies falling just below the midpoint. Although additional interviews conducted after near saturation may result in new information, our findings suggest there may be diminishing returns relative to the resources expended. We have identified several study characteristics that researchers can consider when making decisions on sample size for web-based interviews.

Although our findings were mostly consistent across the 5 studies we examined, near saturation was reached sooner on the studies that consisted of largely deductive codes compared with those that had a greater number of inductive codes. Consequently, researchers should consider their analytic approach when determining sample size. Studies that intend for the coding scheme to be iterative throughout the coding process may want to err on the side of having a slightly higher sample size than if the codebook is expected to consist largely of deductive codes tied to the interview guide.

These studies ranged in length from 30 to 90 minutes, and a majority (n=3) lasted 60 minutes. Although the 90-minute study reached both true saturation and near saturation at the latest point, the shortest interview (at 30 minutes) required the second-highest number of interviews to reach both saturation points. Although the length of the interview may be a minor consideration, the level of structure of the interview guide and the types of codes used seem to be larger drivers.

Our findings point to the need for a slightly higher number of interviews to reach an acceptable level of saturation—categorized by us as near code saturation—than what has been found in other studies. For example, Guest et al [ 15 ] found that 6 interviews were enough to get high-level themes, reaching a plateau at 10 to 12 interviews. Similarly, Young and Casey [ 27 ] found that near code saturation was reached at 6 to 9 interviews.

Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites. Data from our studies included participants from all US Census Bureau regions, which provides support that these findings may be more generalizable than previous studies.

Limitations

Our study had several limitations. First, our analysis was conducted on a sample of 5 studies that had similarities. All the studies were related to the medical field, and our study populations (patients with an identified medical condition and health care providers) were knowledgeable about the topics discussed. Second, all the studies were conducted using semistructured interview guides that leaned toward being more structured (ie, interviewers largely stuck to scripted probes as compared with guides that allow for unscripted follow-up probes and unstructured conversations). Additionally, all the studies used a similar approach to coding by using a mix of both deductive and inductive codes (though to varying extents). Consequently, studies with a less structured approach to both the interview and coding process may yield different results. Finally, all our studies are broadly classified as social science research. The findings for other fields of inquiry, such as economic or medical studies, may differ.

Conclusions

Saturation is an important consideration in planning and conducting qualitative research, yet, there is no definitive guidance on how to define and measure saturation, particularly for web-based data collection, which allows for data to be collected from a more geographically diverse sample. Our study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population being studied. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Rather than trying to reach a consensus on the number of interviews needed to achieve saturation in qualitative research overall, we recommend that future research should explore saturation within different types of studies, such as different fields of inquiry, subject matter, and populations being studied. Creating a robust body of knowledge in this area will allow researchers to identify the guidance that best meets the needs of their work.

Acknowledgments

Research Triangle Institute–affiliated authors received support for the development of this manuscript from the RTI Fellow’s program under RTI Fellow, Leila Kahwati, MPH, MD. All studies included in the analyses were funded by the Food and Drug Administration. The authors would like to thank the following Food and Drug Administration staff for their contribution to this research: Kit Aikin, Kevin Betts, Amie O’Donoghue, and Helen Sullivan.

Data Availability

The data sets analyzed during this study are available from the corresponding author on reasonable request.

Conflicts of Interest

None declared.

Achieving saturation in interviews: saturation type, methods for achieving saturation, and findings by other authors.

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Edited by A Mavragani; submitted 22.09.23; peer-reviewed by K Kelly, G Guest; comments to author 24.10.23; revised version received 30.01.24; accepted 09.05.24; published 09.07.24.

©Claudia M Squire, Kristen C Giombi, Douglas J Rupert, Jacqueline Amoozegar, Peyton Williams. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 09.07.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Adult and Young Adult Reentry Project Grants: Differences in Service Offerings and Implementation Challenges (Issue Brief)

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For two decades, the U.S. Department of Labor (DOL) has invested substantial funding toward programs serving justice-involved individuals. Among its recent investments, DOL awarded over $243 million in Reentry Projects (RP) grant programs between 2017 and 2019 to improve participants’ employment and justice outcomes. DOL prioritized awarding grants to programs that were evidence-informed, and many went to experienced providers. They were awarded across a broad range of intermediaries and non-profit community-based organizations serving a total of 17,361 participants across 34 states, Washington DC, and Puerto Rico. RP grants were 36-39 months long and were at different phases when the COVID-19 pandemic began in March 2020. RP grantees served a total of 9,098 adults (individuals over 24) and 8,263 young adults (individuals between ages 18 and 24) after their release from jail or prison.

In 2017, the Chief Evaluation Office, in collaboration with the Employment and Training Administration funded the Reentry Project Grants Evaluation. This implementation and impact evaluation aims to identify and evaluate promising practices used in reentry employment programs, which are comprehensive strategies to address the range of challenges formerly incarcerated adults and young adults who have been involved in the justice system face in making a successful transition back to the community.

This issue brief describes the differences and similarities between adult and young adult grantees in terms of the services they offered, and the implementation challenges they reported. The analysis draws on quantitative data from a survey of all 116 organizations that received RP grants. Data from the grantee survey were analyzed using descriptive statistics as well as chi-squared tests to determine whether differences across grant types were statistically significant. The brief also draws on in-depth qualitative data from a subset of nine grantees that received both adult and young adult grants.

Some key findings from RP grantee survey include:

• While both adult and young adult grantees relied heavily on referrals from criminal justice system partner agencies, a higher proportion of young adult CBO grantees than adult CBO grantees indicated that community outreach efforts were their largest referral source and a lower proportion of young adult CBO grantees reported word-of-mouth as their largest source of referrals compared with adult CBO grantees.
• Analysis of grantee survey and qualitative data suggest that adult and young adult services may differ in four key areas: (1) positive youth development components (2) legal services for child support and diversion, (3) educational services and requirements, and (4) program length.
• Young adult grantees reported the following challenges significantly more often than adult grantees: recruiting participants, engaging, and retaining those participants throughout the course of service delivery, and placing participants in employment.