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Organizing Your Social Sciences Research Paper

  • Independent and Dependent Variables
  • Purpose of Guide
  • Design Flaws to Avoid
  • Glossary of Research Terms
  • Reading Research Effectively
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • Extending the Timeliness of a Topic Idea
  • Academic Writing Style
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • Research Process Video Series
  • Executive Summary
  • The C.A.R.S. Model
  • Background Information
  • The Research Problem/Question
  • Theoretical Framework
  • Citation Tracking
  • Content Alert Services
  • Evaluating Sources
  • Primary Sources
  • Secondary Sources
  • Tiertiary Sources
  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Quantitative Methods
  • Insiderness
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism
  • Footnotes or Endnotes?
  • Further Readings
  • Generative AI and Writing
  • USC Libraries Tutorials and Other Guides
  • Bibliography

Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

  • You need to understand and be able to evaluate their application in other people's research.
  • You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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  • v.37(16); 2022 Apr 25

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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 .

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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.

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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.

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  • Types of Variables in Research | Definitions & Examples

Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

  • What type of data does the variable contain?
  • What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

  • Quantitative data represents amounts.
  • Categorical data represents groupings.

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variable can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables.

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is colour-coded according to the type of variable: nominal , continuous , ordinal , and binary .

Example data sheet showing types of variables in a plant salt tolerance experiment

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

Example of a data sheet showing dependent and independent variables for a plant salt tolerance experiment.

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases, you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e., the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variable are listed below.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Discrete and continuous variables are two types of quantitative variables :

  • Discrete variables represent counts (e.g., the number of objects in a collection).
  • Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

  • The  independent variable  is the amount of nutrients added to the crop field.
  • The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

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Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

  • The independent variable is the cause. Its value is independent of other variables in your study.
  • The dependent variable is the effect. Its value depends on changes in the independent variable.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Table of contents

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

  • Explanatory variables (they explain an event or outcome)
  • Predictor variables (they can be used to predict the value of a dependent variable)
  • Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

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There are two main types of independent variables.

  • Experimental independent variables can be directly manipulated by researchers.
  • Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

  • A low-dose experimental group
  • A high-dose experimental group
  • A placebo group (to research a possible placebo effect )

Independent and dependent variables

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

In statistics , dependent variables are also called:

  • Response variables (they respond to a change in another variable)
  • Outcome variables (they represent the outcome you want to measure)
  • Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

  • Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
  • Does this variable come before the other variable in time?
  • Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

  • Is this variable measured as an outcome of the study?
  • Is this variable dependent on another variable in the study?
  • Does this variable get measured only after other variables are altered?

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Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

  • your variable types
  • level of measurement
  • number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

  • A bar chart is ideal when you have a categorical independent variable.
  • A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

independent and dependent variables

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.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

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

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

  • The type of soda – diet or regular – is the independent variable .
  • The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

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References/Further Reading

Bollen K. A. (1989). Structural equations with latent variables . Wiley George and Mallery.

Google Scholar  

George, D., & Mallery, M. (2010). SPSS for windows step by step: A simple guide and reference, 17.0 update (10a ed.). Pearson.

Ghasemi, A., & Saleh Zahedias, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism. 2012 Spring; 10 (2), 486–489. https://doi.org/10.5812/ijem.3505 . (Published online 2012 Apr 20).

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An Assessment of the Use of Partial Least Squares Structural Equation Modeling in Marketing Research. Journal of the Academy of Marketing Science, 40 , 414–433. https://doi.org/10.1007/s11747-011-0261-6 .

Marshal, E. (2021). The Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests. University of Sheffield. https://www.statstutor.ac.uk/resources/uploaded/tutorsquickguidetostatistics.pdf .

Patten, M. L. (2014). Understanding research methods: An overview of the essentials . Pyrczak Publishing.

Saliya, C. A. (2020a). Dynamics of credit decision-making: a taxonomy and a typological matrix. Review of Behavioral Finance . https://doi.org/10.1108/RBF-07-2019-0092 .

Saliya, C. A. (2020b). Stock Market Development and Nexus of Market Liquidity. International Journal of Finance and Economics . https://doi.org/10.1002/ijfe.2376 .

Saliya, C. A. (2021b). Driving Forces of Individual Investors in Stock Market Participation. Review of Economics and Finance, 19 , 73–79. https://refpress.org/ref-vol19-a8/ .

Yapa, B. W., & Simb, C. H. (2011). Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation. 81 (12). https://www.tandfonline.com/doi/pdf/ , https://doi.org/10.1080/00949655.2010.520163 .

Youtube Links

Calculating and Interpreting Cronbach's Alpha Using SPSS https://www.youtube.com/watch?v=Kz8OdR6lV44 .

Checking that data is normally distributed using SPSS https://www.youtube.com/watch?v=yTLv91TAngM .

Flow chart for choosing tests https://www.youtube.com/watch?v=tfiDu--7Gmg .

Jarque-Bera test in MS Excel https://www.youtube.com/watch?v=3or8IwMUgjs .

Learn SPSS in 15 minutes https://www.youtube.com/watch?v=TZPyOJ8tFcI&t=738s .

Normal Distribution Definition and Properties https://www.youtube.com/watch?v=iMak-EW4HtM .

Normality test using SPSS: How to check whether data are normally distributed https://www.youtube.com/watch?v=IiedOyglLn0 .

SPSS Tutorial for data analysis | SPSS for Beginners : https://www.youtube.com/watch?v=Bku1p481z80&t=29s

SPSS for Beginners https://www.youtube.com/watch?v=hKA2VQ60bxg .

SPSS step by step Tutorials https://www.spss-tutorials.com/basics/ .

SPSS step by step application https://statistics.laerd.com/features-overview.php .

Types of Data: Categorical vs Numerical Data : https://www.youtube.com/watch?v=DUcXZ08IdMo .

Types of Data: Nominal, Ordinal, Interval/Ratio https://www.youtube.com/watch?v=hZxnzfnt5v8 .

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Types of Variables in Research – Definition & Examples

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types-of-variables-in-research-Definition

A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes.

Inhaltsverzeichnis

  • 1 Types of Variables in Research – In a Nutshell
  • 2 Definition: Types of variables in research
  • 3 Types of variables in research – Quantitative vs. Categorical
  • 4 Types of variables in research – Independent vs. Dependent
  • 5 Other useful types of variables in research

Types of Variables in Research – In a Nutshell

  • A variable is an attribute of an item of analysis in research.
  • The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative .
  • The types of variables in research (correlational) can be classified into predictor or outcome variables.
  • Other types of variables in research are confounding variables , latent variables , and composite variables.

Definition: Types of variables in research

A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study.

Note that the correct variable will help with your research design , test selection, and result interpretation.

In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.

Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents.

Types of variables in research – Quantitative vs. Categorical

Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes.

Quantitative or numerical data represents amounts, while categorical data represents collections or groupings.

The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better.

Quantitative variables

The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables .

The table below explains the elements that set apart discrete and continuous types of variables in research:

Categorical variables

Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts.

There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary.

It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete.

Data sheet of quantitative and categorical variables

A data sheet is where you record the data on the variables in your experiment.

In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet.

The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process.

Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research.

Ireland

Types of variables in research – Independent vs. Dependent

types-of-variables-in-research-Dependent-independet-and-constant-variable

The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival.

Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant.

The table below summarizes independent variables, dependent variables , and control variables .

Data sheet of independent and dependent variables

In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent.

Below is a data sheet based on our experiment:

Types of variables in correlational research

The types of variables in research may differ depending on the study.

In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables.

However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable.

Other useful types of variables in research

The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study.

Below are other types of variables in research worth understanding.

What is the definition for independent and dependent variables?

An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study.

What are quantitative and categorical variables?

Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design.

Discrete and continuous variables: What is their difference?

Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight.

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Data Analysis in Research: Types & Methods

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Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

  • Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
  • Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
  • Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

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The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

  • Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
  • Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
  • Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
  • Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

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Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

  • Fraud: To ensure an actual human being records each response to the survey or the questionnaire
  • Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
  • Procedure: To ensure ethical standards were maintained while collecting the data sample
  • Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

  • Count, Percent, Frequency
  • It is used to denote home often a particular event occurs.
  • Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

  • Mean, Median, Mode
  • The method is widely used to demonstrate distribution by various points.
  • Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

  • Range, Variance, Standard deviation
  • Here the field equals high/low points.
  • Variance standard deviation = difference between the observed score and mean
  • It is used to identify the spread of scores by stating intervals.
  • Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

  • Percentile ranks, Quartile ranks
  • It relies on standardized scores helping researchers to identify the relationship between different scores.
  • It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

  • Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
  • Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

  • Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
  • Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
  • Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
  • Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
  • Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
  • Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection  methods, and choose samples.

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  • The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
  • Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
  • The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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What Is Analysis of Variance (ANOVA)?

The formula for anova is:, what does the analysis of variance reveal, example of how to use anova, one-way anova versus two-way anova, how does anova differ from a t test, what is analysis of covariance (ancova), does anova rely on any assumptions, the bottom line.

  • Fundamental Analysis

Analysis of Variance (ANOVA) Explanation, Formula, and Applications

Learn how to use this statistical analysis tool

analysis of variables research

Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.

The t- and z-test methods developed in the 20th century were used for statistical analysis until 1918, when Ronald Fisher created the analysis of variance method. ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. The term became well-known in 1925, after appearing in Fisher's book, "Statistical Methods for Research Workers." It was employed in experimental psychology and later expanded to subjects that were more complex.

Key Takeaways

  • Analysis of variance, or ANOVA, is a statistical method that separates observed variance data into different components to use for additional tests.
  • A one-way ANOVA is used for three or more groups of data, to gain information about the relationship between the dependent and independent variables.
  • If no true variance exists between the groups, the ANOVA's F-ratio should equal close to 1.

 F = MST MSE where: F = ANOVA coefficient MST = Mean sum of squares due to treatment MSE = Mean sum of squares due to error \begin{aligned} &\text{F} = \frac{ \text{MST} }{ \text{MSE} } \\ &\textbf{where:} \\ &\text{F} = \text{ANOVA coefficient} \\ &\text{MST} = \text{Mean sum of squares due to treatment} \\ &\text{MSE} = \text{Mean sum of squares due to error} \\ \end{aligned} ​ F = MSE MST ​ where: F = ANOVA coefficient MST = Mean sum of squares due to treatment MSE = Mean sum of squares due to error ​ 

The ANOVA test is the initial step in analyzing factors that affect a given data set. Once the test is finished, an analyst performs additional testing on the methodical factors that measurably contribute to the data set's inconsistency. The analyst utilizes the ANOVA test results in an f-test to generate additional data that aligns with the proposed regression models.

The ANOVA test allows a comparison of more than two groups at the same time to determine whether a relationship exists between them. The result of the ANOVA formula, the F statistic (also called the F-ratio), allows for the analysis of multiple groups of data to determine the variability between samples and within samples.

If no real difference exists between the tested groups, which is called the null hypothesis , the result of the ANOVA's F-ratio statistic will be close to 1. The distribution of all possible values of the F statistic is the F-distribution. This is actually a group of distribution functions, with two characteristic numbers, called the numerator degrees of freedom and the denominator degrees of freedom.

A researcher might, for example, test students from multiple colleges to see if students from one of the colleges consistently outperform students from the other colleges. In a business application, an R&D researcher might test two different processes of creating a product to see if one process is better than the other in terms of cost efficiency.

The type of ANOVA test used depends on a number of factors. It is applied when data needs to be experimental. Analysis of variance is employed if there is no access to statistical software resulting in computing ANOVA by hand. It is simple to use and best suited for small samples. With many experimental designs, the sample sizes have to be the same for the various factor level combinations.

ANOVA is helpful for testing three or more variables. It is similar to multiple two-sample t-tests . However, it results in fewer type I errors and is appropriate for a range of issues. ANOVA groups differences by comparing the means of each group and includes spreading out the variance into diverse sources. It is employed with subjects, test groups, between groups and within groups.

There are two main types of ANOVA: one-way (or unidirectional) and two-way. There also variations of ANOVA. For example, MANOVA (multivariate ANOVA) differs from ANOVA as the former tests for multiple dependent variables simultaneously while the latter assesses only one dependent variable at a time. One-way or two-way refers to the number of independent variables in your analysis of variance test. A one-way ANOVA evaluates the impact of a sole factor on a sole response variable. It determines whether all the samples are the same. The one-way ANOVA is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups.

A two-way ANOVA is an extension of the one-way ANOVA. With a one-way, you have one independent variable affecting a dependent variable. With a two-way ANOVA, there are two independents. For example, a two-way ANOVA allows a company to compare worker productivity based on two independent variables, such as salary and skill set. It is utilized to observe the interaction between the two factors and tests the effect of two factors at the same time.

ANOVA differs from T tests in that ANOVA can compare three or more groups while T tests are only useful for comparing two groups at one time.

Analysis of Covariance combines ANOVA and regression. It can be useful for understanding within-group variance that ANOVA tests do not explain.

Yes, ANOVA tests assume that the data is normally distributed and that the levels of variance in each group is roughly equal. Finally, it assumes that all observations are made independently. If these assumptions are not accurate, ANOVA may not be useful for comparing groups.

ANOVA is a good way to compare more than two groups to identify relationships between them. The technique can be used in scholarly settings to analyze research or in the world of finance to try to predict future movements in stock prices. Understanding how ANOVA works and when it may be a useful tool can be helpful for advanced investors.

Genetic Epidemiology, Translational Neurogenomics, Psychiatric Genetics and Statistical Genetics-QIMR Berghofer Medical Research Institute. " The Correlation Between Relatives on the Supposition of Mendelian Inheritance ."

Encyclopaedia Britannica. " Sir Ronald Aylmer Fisher ."

Ronald Fisher. " Statistical Methods for Research Workers ." Springer-Verlag New York, 1992.

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Variable and its Types in Statistical Analysis

Published by Alvin Nicolas at September 22nd, 2021 , Revised On February 10, 2023

You might have come across the word  variable  in algebra a million times. The case is a little different in statistics. And, you will find that out in a moment.

This guide provides an outline of different types of variables in the statistical analysis , along with examples. But before that, let us first describe a variable.

What is a Variable?

A variable is an attribute to which different values can be assigned. The value can be a characteristic, a number, or a quantity that can be counted. It is sometimes called a data item. To precisely say, it is anything accepting various values. Examples of variables can be gender, expenses, hair colour, number of schools in a city, and so on.

Though there are many types of variables in statistics, they are broadly divided into four categories or groups in statistics. These are:

  • Quantitative Variables
  • Categorical Variables

Dependent Variables

Independent variables.

Quantitative Variables

Quantitative Variables:

Quantitative Variables are those variables that can be counted in terms of figures and numbers. They are also called Numeric Variables. A person’s height is one example of a quantitative variable because it can take on different values. You can be 3 ft., 5 ft., and so on.

These properties or characteristics can vary from person to person. Thus, quantitative variables possess a natural ranking or order. They are further divided into two more categories, namely  Discrete  and  Continuous  Variables.

Discreet Variables:

All the values that are countable and can have a finite number of possibilities are called discreet variables. They are usually integers. For instance, citizens of a city, teachers in a school, and the number of children in the family.

Now you must be wondering how someone can count the total number of citizens in a city. Well, that is something tedious, challenging, and time-consuming, but not impossible.

Continuous Variables:

Continuous variables, on the other hand, are numbers that can take forever to count. An example of a continuous variable is a person’s age or time, both of which you cannot count. Why not? Because they cannot stay fixed. A person’s age could be 35 years, two months, 12 hours, 4 minutes, 10 seconds, nanoseconds, picoseconds, and so on. However, there is a way to make it a little convenient. And that is, by first turning it into a discrete variable and then doing the measuring and counting.

Not sure if you get this?

Here’s an example that can help you comprehend.

You cannot count the age of a person or time in general, but if you change it to:

  • A person’s age in 5 years
  • Hours, minutes, and seconds till 10 pm tomorrow

You can get accurate values this way.

Categorical Variables:

Categorical variables belong to a broad group of variables that are not numbers and can never be counted. They are also popularly known as Attribute or Quantitative Variables. They are further categorized into three types:

  • Nominal Variables
  • Ordinal Variables
  • Dichotomous Variables

Let’s dig deeper into each of these.

Nominal Variables:

Nominal variables are those variable that is used to label and group various attributes being measured. It takes qualitative values to reflect different categories, and there is no intrinsic ranking of these categories. For instance:

Ordinal Variables:

Ordinal variables, on the contrary, are those where the order of values matters but not the difference between these values. For instance:

  • Educational Level
  • Hierarchy in Office Positions
  • Income Level

An important thing to consider here is that difference between adjacent values does not have to have the same meaning. For example, the difference between the two salary levels, “Less than 30K” and “30K-60K,” does not have the same meaning as the difference between the two salary packages “30K-60K” and “more than 60K”.

Dichotomous Variables:

The name is self-explanatory! A dichotomous variable has only two (di) levels or categories. For instance, if you ask somebody if they own a car or not, it would have two possible answers. Similarly, say you hear about birth in your family, then you know it would either mean a girl is born or a boy.

The independent variables, also known as  resultant variables   and   experimental variables , are computed in research to depict the effects of dependent variables . That is why in statistics, we say that they are determinants of the value of the dependent variable.

In other words, the variations of these variables do not depend on another variable. They have measured inputs whose variation solely depends on the person dealing with the variables.

For instance, say you are required to move an object from point A to be in a given time. The independent variable, in this case, would be the speed which depends on the person running/walking, while the dependent variable is the time that changes with the change in speed of the person.

You might have guessed what these variables are by now with everything we have discussed earlier.

Well, you are right!

Dependent variables are the complete opposite of dependent variables, which means their values change with another variable. So, if the value of the independent variable changes, the dependent variable will also vary.

But how is this change determined?

The course of this change can be found out by a function representing the relationship between the independent and dependent variables. In mathematics, it is expressed as a function of the independent variable. For instance, if z =h(x) = 4x+5, then ‘z’ here would be the dependent variable and ‘x’ is the independent one.

Other types of variables you might also come across:

Latent Variables:

The latent variable is one that cannot be directly observed. They are hidden!

So, how do you detect these variables?

You can detect this variable from the effects they have on other variables. For instance, a person’s overall health is a latent variable because it can only be detected by measuring the physical properties of the body. These include the body temperature, sugar level, blood pressure, and so on.

Composite Variables:

It is the addition of several other variables in an experiment. For instance, BMI can be a composite variable as you need a person’s height and weight for it.

Confounding Variables:

A confounding variable is the crux of all your problems.

Yes, you heard it right!

These are extra variables that you did not account for and can ruin the whole experiment. They trick you by suggesting a correlation between different variables when in reality, that is never the case. For instance, if you are doing some research on whether lack of exercise can lead to weight gain or not. Lack of exercise here is the independent variable, weight gain is the dependent variable, while any other variable such as the effect of weather conditions would be a confounding variable.

What is a discreet variable?

A discreet variable is one that you can measure of count in a finite amount of time. For instance, the number of students in your class.

What are the differences between quantitative and categorical variables?

Quantitative variables represent some measurements and take numerical values. For instance, the age of a person. Categorical variables, on the other hand, are not countable and place individuals into several other categories. For instance, the gender of a person. It cannot be counted; instead, it is divided into two or more labels, such as male, female and other.

What are dependent and independent variables?

A dependent variable is one directly relying on the independent variable. For instance, if you are reading a novel and have to finish it in a given timeline. Time here would be the dependent variable, and the speed or number of pages you read will be the independent variable.

What is an example of a dichotomous variable?

A dichotomous variable is one where there are two categories, both of equal significance. For example, if your survey question says, “are you a smoker?” it could either be ‘yes’ or ‘no’.

For queries and questions, please leave a comment below and we will get back to you soon.

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The Essential Guide to Independent and Dependent Variables in Data Analysis

You will learn the critical differences and applications of independent and dependent variables in data science.

Introduction

In data analysis, independent and dependent variables are the backbone of understanding how various elements interact within a study. Whether you’re a student stepping into the world of research, a seasoned data scientist, or a professional analyzing business trends, grasping the roles of these variables is crucial.

Independent variables, often predictors or causes, are the factors that we expect to influence outcomes. They are the variables that researchers manipulate or select in an experiment to observe their effect on other variables. On the other hand, dependent variables are those outcomes or effects that are influenced or changed due to the manipulation of the independent variables. They are what researchers measure in an experiment.

The distinction and interaction between these two variables are foundational across diverse research fields – from psychological studies to biological experiments and from market research to technological advancements. Their correct identification and application determine a study’s direction and the validity of its conclusions. This guide aims to demystify these concepts, highlighting their critical roles in experimental design and data analysis. As we delve into the specifics of independent and dependent variables, you will gain insights essential for aspiring or professional data analysts.

  • Independent variables are the predictors or causes in a study, shaping the outcomes.
  • Dependent variables change in response to the independent variable’s influence.
  • The relationship between these variables is foundational in experimental designs.
  • Misidentifying these variables can lead to incorrect data interpretations.
  • These variables are essential in regression analysis, determining causation.

Understanding Independent Variables

Defining independent variables in research.

Independent variables stand at the forefront of experimentation and analysis in the research world. These are the variables that researchers actively manipulate or choose to observe their impact on other variables, commonly known as dependent variables. The role of an independent variable is to provide a basis for comparison and to drive the experiment or study forward. Its manipulation or variation allows researchers to observe changes, draw conclusions, and predict the behavior of the dependent variables.

Independent Variables in Various Contexts

The nature of independent variables can vary greatly depending on the field of study. For example, in a clinical trial, the independent variable might be a new medication or treatment method. In a psychological study, it could be a specific therapeutic intervention. In economics, it might be a change in interest rates. These examples illustrate how independent variables are not confined to any discipline but are fundamental to research across all science and social science domains.

The Importance of Correct Identification

Correctly identifying the independent variable in a study is a critical step in research design. Misidentification can lead to flawed experiments and inaccurate conclusions. It is the influence or change of the independent variable that researchers seek to understand about the dependent variable. This relationship is the cornerstone of hypothesis testing, where researchers form predictions about how changes in the independent variable will affect the dependent variable. Therefore, accurately identifying the independent variable directly impacts the validity and reliability of the research findings.

Exploring Dependent Variables

Defining dependent variables and their distinction from independent variables.

In the data analysis landscape, dependent variables emerge as the responses or effects influenced by independent variables. These are the outcomes that researchers measure and analyze to understand the impact of changes in the independent variables. Unlike independent variables, which are manipulated or chosen by the researcher, dependent variables are observed to see how they respond to these manipulations. This distinction is crucial as it sets the stage for effective research design and data interpretation.

Examples of Dependent Variables Across Different Fields

Dependent variables manifest in various forms across different research disciplines. In a medical study, a dependent variable could be the patient’s response to a treatment, measured in terms of recovery rates or symptom reduction. In an educational setting, student performance scores can be a dependent variable, changing in response to different teaching methods (the independent variable). In environmental research, a lake’s pollution level could be dependent on factors like industrial activity. These examples underscore the breadth of dependent variables’ applicability, showcasing their pivotal role in diverse research contexts.

Implications of Dependent Variables in Data Interpretation

The correct interpretation of dependent variables is a cornerstone of research. Through these variables, the effectiveness or impact of the independent variable is gauged. Misinterpretation or incorrect measurement of dependent variables can lead to faulty conclusions, potentially skewing the entire outcome of a study. Hence, understanding the nature, variability, and response patterns of dependent variables is imperative. Researchers must rigorously analyze these variables to draw reliable and valid conclusions, advancing knowledge in their field of study.

The Relationship Between Independent and Dependent Variables

Interaction of independent and dependent variables in research.

The interaction between independent and dependent variables forms the crux of scientific inquiry and data analysis. This interaction is a simple cause-and-effect relationship and a nuanced interplay that shapes research outcomes. Researchers manipulate or alter independent variables to observe their effect on dependent variables. The response of the dependent variable to these manipulations reveals critical insights, enabling researchers to understand and quantify the relationship between the two.

Significance in Experimental Design

In experimental design, the relationship between independent and dependent variables is paramount. This relationship directs the structure of the experiment, influencing everything from the hypothesis formation to the method of data collection and analysis. The clarity of this relationship determines the experiment’s ability to test hypotheses accurately and yield meaningful results. It also influences the choice of statistical methods used for analysis, as different types of relationships may require different analytical approaches.

Practical Examples and Case Studies

To illustrate this relationship, consider a study in agricultural science where the growth of a crop (dependent variable) is analyzed in response to different fertilizer types (independent variable). Another example is psychology, where a researcher might examine the impact of therapy methods (independent variable) on patient stress levels (dependent variable). These practical examples highlight how the interplay between independent and dependent variables is critical in deriving conclusions and advancing knowledge in various fields.

Common Misconceptions and Pitfalls

Addressing common misunderstandings about independent and dependent variables.

One prevalent misconception is that independent and dependent variables are inherently related in a causal relationship. While this can be true in experimental designs, it is not a universal rule. In observational studies, these variables may show correlation without causation. Another standard error is assuming that these variables are static throughout different phases of research. Their roles can be context-dependent and vary according to the study’s design and objectives.

Consequences of Misidentifying Independent and Dependent Variables

Misidentifying these variables can significantly impact the integrity and outcomes of a research study. When the independent variable is incorrectly identified, the study might fail to address the research question effectively, leading to invalid conclusions. Similarly, incorrect identification of a dependent variable can result in inaccurate measurements and data analysis, skewing the study’s results. Such errors undermine the research’s validity and can lead to wasted resources and misinformed decisions based on the findings.

Tips on Avoiding These Pitfalls in Research

To avoid these pitfalls, researchers should:

1. Clearly Define Research Questions:  A well-structured research question helps correctly identify the variables.

2. Understand the Study Design:  Different designs (experimental, observational) impact the roles of these variables.

3. Seek Peer Input:  Collaborating or consulting with peers can provide a fresh perspective and help identify any oversights in variable identification.

4. Review Literature:  Examining similar studies can offer insights into appropriate variable identification and usage.

5. Pilot Studies:  Conducting preliminary studies or pilot tests can help clarify the roles of variables before the full-scale research.

This comprehensive guide has navigated the intricate world of independent and dependent variables, laying a foundation for understanding their pivotal roles in data analysis. We began by defining these variables and establishing how independent variables act as influencers in research. The dependent variables are the subjects of influence, changing in response to the former. This semantic distinction forms the bedrock of experimental and observational studies across various disciplines.

We explored how these variables function in different contexts, showing their universal applicability, from clinical trials in medicine to economic analyses. The importance of correctly identifying these variables was underscored, highlighting how misidentification can lead to flawed conclusions and ineffective research.

Our journey delved into the relationship between these variables, emphasizing their interplay as the essence of scientific inquiry. We addressed common misconceptions, shedding light on the nuances of their interaction, and provided practical advice to avoid pitfalls in research.

In advanced analysis scenarios, like regression, we discussed the enhanced roles of independent and dependent variables. These scenarios demonstrate the complexities of data interpretation and the need for precise variable analysis, especially in the evolving landscape of data science.

The insights provided in this guide are essential for anyone engaged in data analysis, from students to seasoned professionals. Understanding the dynamics of independent and dependent variables is not just about mastering a concept; it’s about equipping oneself with the tools to uncover truths, make informed decisions, and contribute meaningfully to the vast field of research.

As we conclude, remember that the concepts of independent and dependent variables are more than terminologies; they are the lenses through which we can view and understand the complex patterns and relationships in data. Embracing this understanding will undoubtedly enhance your capabilities in data analysis, research design, and beyond.

Recommended Articles

Explore more in-depth articles on data analysis and variable interactions on our blog for enhanced learning and application.

  • What Makes a Variable Qualitative or Quantitative?
  • What is an Independent Variable in an Experiment?

In Science, What is a Dependent Variable?

Frequently asked questions (faqs).

Q1: What is an Independent Variable?  It’s a variable in research manipulated or controlled to see its effect on a dependent variable.

Q2: What is a Dependent Variable?  This variable is observed and measured to see the effect of an independent variable.

Q3: How do Independent and Dependent Variables Interact?  The independent variable is thought to influence or cause changes in the dependent variable.

Q4: Why are These Variables Important in Research?  Understanding these variables is crucial for designing experiments and interpreting results accurately.

Q5: Can There Be More Than One Independent Variable in an Experiment?  Yes, experiments can have multiple independent variables to explore complex relationships.

Q6: How Do You Identify These Variables in a Study?  Identify the cause (independent) and effect (dependent) elements in the research question.

Q7: What are Examples of Independent and Dependent Variables?  In a study on education, teaching methods could be independent, and student performance could be dependent.

Q8: How Do These Variables Affect Data Analysis?  Correct identification is essential for accurate statistical analysis and drawing valid conclusions.

Q9: Can a Variable be Both Independent and Dependent?  In different studies or contexts, the same variable might play different roles.

Q10: Why is the Distinction Between These Variables Critical?  Understanding their roles helps in forming hypotheses and interpreting data in research.

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Variables: Definition, Examples, Types of Variables in Research

Variables: Definition, Examples, Types Of Variables In Research

What is a Variable?

Within the context of a research investigation, concepts are generally referred to as variables. A variable is, as the name applies, something that varies.

Examples of Variable

These are all examples of variables because each of these properties varies or differs from one individual to another.

  • income and expenses,
  • family size,
  • country of birth,
  • capital expenditure,
  • class grades,
  • blood pressure readings,
  • preoperative anxiety levels,
  • eye color, and
  • vehicle type.

What is Variable in Research?

A variable is any property, characteristic, number, or quantity that increases or decreases over time or can take on different values (as opposed to constants, such as n , that do not vary) in different situations.

When conducting research, experiments often manipulate variables. For example, an experimenter might compare the effectiveness of four types of fertilizers.

In this case, the variable is the ‘type of fertilizers.’ A social scientist may examine the possible effect of early marriage on divorce. Her early marriage is variable.

A business researcher may find it useful to include the dividend in determining the share prices . Here, the dividend is the variable.

Effectiveness, divorce, and share prices are variables because they also vary due to manipulating fertilizers, early marriage, and dividends .

11 Types of Variables in Research

Qualitative variables.

An important distinction between variables is the qualitative and quantitative variables.

Qualitative variables are those that express a qualitative attribute, such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.

The value of the variable ‘religion’ (Muslim, Hindu.., etc..) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.

For example, the variable sex has two distinct categories: ‘male’ and ‘female.’ Since the values of this variable are expressed in categories, we refer to this as a categorical variable.

Similarly, the place of residence may be categorized as urban and rural and thus is a categorical variable.

Categorical variables may again be described as nominal and ordinal.

Ordinal variables can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., and clothing size (Extra large, large, medium, small).

Nominal variables are those that can neither be ranked nor logically ordered, such as religion, sex, etc.

A qualitative variable is a characteristic that is not capable of being measured but can be categorized as possessing or not possessing some characteristics.

Quantitative Variables

Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.

Age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable because a family might be comprised of one, two, or three members, and so on.

Each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call quantitative or sometimes numeric variables.

A quantitative variable is one for which the resulting observations are numeric and thus possess a natural ordering or ranking.

Discrete and Continuous Variables

Quantitative variables are again of two types: discrete and continuous.

Variables such as some children in a household or the number of defective items in a box are discrete variables since the possible scores are discrete on the scale.

For example, a household could have three or five children, but not 4.52 children.

Other variables, such as ‘time required to complete an MCQ test’ and ‘waiting time in a queue in front of a bank counter,’ are continuous variables.

The time required in the above examples is a continuous variable, which could be, for example, 1.65 minutes or 1.6584795214 minutes.

Of course, the practicalities of measurement preclude most measured variables from being continuous.

Discrete Variable

A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size and a number of defective items in a box. They are often the results of enumeration or counting.

A few more examples are;

  • The number of accidents in the twelve months.
  • The number of mobile cards sold in a store within seven days.
  • The number of patients admitted to a hospital over a specified period.
  • The number of new branches of a bank opened annually during 2001- 2007.
  • The number of weekly visits made by health personnel in the last 12 months.

Continuous Variable

A continuous variable may take on an infinite number of intermediate values along a specified interval. Examples are:

  • The sugar level in the human body;
  • Blood pressure reading;
  • Temperature;
  • Height or weight of the human body;
  • Rate of bank interest;
  • Internal rate of return (IRR),
  • Earning ratio (ER);
  • Current ratio (CR)

No matter how close two observations might be, if the instrument of measurement is precise enough, a third observation can be found, falling between the first two.

A continuous variable generally results from measurement and can assume countless values in the specified range.

Dependent Variables and Independent Variable

In many research settings, two specific classes of variables need to be distinguished from one another: independent variable and dependent variable.

Many research studies aim to reveal and understand the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.

Look at the following statements:

  • Low intake of food causes underweight.
  • Smoking enhances the risk of lung cancer.
  • Level of education influences job satisfaction .
  • Advertisement helps in sales promotion .
  • The drug causes improvement of health problems.
  • Nursing intervention causes more rapid recovery.
  • Previous job experiences determine the initial salary.
  • Blueberries slow down aging.
  • The dividend per share determines share prices.

In each of the above queries, we have two independent and dependent variables. In the first example, ‘low intake of food’ is believed to have caused the ‘problem of being underweight.’

It is thus the so-called independent variable. Underweight is the dependent variable because we believe this ‘problem’ (the problem of being underweight) has been caused by ‘the low intake of food’ (the factor).

Similarly, smoking, dividend, and advertisement are all independent variables, and lung cancer, job satisfaction, and sales are dependent variables.

In general, an independent variable is manipulated by the experimenter or researcher, and its effects on the dependent variable are measured.

Independent Variable

The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.

The definition implies that the experimenter uses the independent variable to describe or explain its influence or effect of it on the dependent variable.

Variability in the dependent variable is presumed to depend on variability in the independent variable.

Depending on the context, an independent variable is sometimes called a predictor variable, regressor, controlled variable, manipulated variable, explanatory variable, exposure variable (as used in reliability theory), risk factor (as used in medical statistics ), feature (as used in machine learning and pattern recognition) or input variable.

The explanatory variable is preferred by some authors over the independent variable when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.

If the independent variable is referred to as an explanatory variable, then the term response variable is preferred by some authors for the dependent variable.

Dependent Variable

The variable used to describe or measure the problem or outcome under study is called a dependent variable.

In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.

A business researcher may find it useful to include the dividend in determining the share prices. Here dividend is the independent variable, while the share price is the dependent variable.

The dependent variable usually is the variable the researcher is interested in understanding, explaining, or predicting.

In lung cancer research, the carcinoma is of real interest to the researcher, not smoking behavior per se. The independent variable is the presumed cause of, antecedent to, or influence on the dependent variable.

Depending on the context, a dependent variable is sometimes called a response variable, regressand, predicted variable, measured variable, explained variable, experimental variable, responding variable, outcome variable, output variable, or label.

An explained variable is preferred by some authors over the dependent variable when the quantities treated as dependent variables may not be statistically dependent.

If the dependent variable is referred to as an explained variable, then the term predictor variable is preferred by some authors for the independent variable.

Levels of an Independent Variable

If an experimenter compares an experimental treatment with a control treatment, then the independent variable (a type of treatment) has two levels: experimental and control.

If an experiment were to compare five types of diets, then the independent variables (types of diet) would have five levels.

In general, the number of levels of an independent variable is the number of experimental conditions.

Background Variable

In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.

These variables are often related to many independent variables, so they indirectly influence the problem. Hence they are called background variables.

The background variables should be measured if they are important to the study. However, we should try to keep the number of background variables as few as possible in the interest of the economy.

Moderating Variable

In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur.

In simple relationships, all other variables are extraneous and are ignored.

In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to explain the relationship better.

This emphasizes the need to consider a second independent variable that is expected to have a significant contributory or contingent effect on the originally stated dependent-independent relationship.

Such a variable is termed a moderating variable.

Suppose you are studying the impact of field-based and classroom-based training on the work performance of health and family planning workers. You consider the type of training as the independent variable.

If you are focusing on the relationship between the age of the trainees and work performance, you might use ‘type of training’ as a moderating variable.

Extraneous Variable

Most studies concern the identification of a single independent variable and measuring its effect on the dependent variable.

But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.

Extraneous variables are not necessarily part of the study. They exert a confounding effect on the dependent-independent relationship and thus need to be eliminated or controlled for.

An example may illustrate the concept of extraneous variables. Suppose we are interested in examining the relationship between the work status of mothers and breastfeeding duration.

It is not unreasonable in this instance to presume that the level of education of mothers as it influences work status might have an impact on breastfeeding duration too.

Education is treated here as an extraneous variable. In any attempt to eliminate or control the effect of this variable, we may consider this variable a confounding variable.

An appropriate way of dealing with confounding variables is to follow the stratification procedure, which involves a separate analysis of the different levels of lies in confounding variables.

For this purpose, one can construct two cross­tables for illiterate mothers and the other for literate mothers.

Suppose we find a similar association between work status and duration of breast­feeding in both the groups of mothers. In that case, we conclude that mothers’ educational level is not a confounding variable.

Intervening Variable

Often an apparent relationship between two variables is caused by a third variable.

For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.

An intervening variable theoretically affects the observed phenomena but cannot be seen, measured, or manipulated directly; its effects can only be inferred from the effects of the independent and moderating variables on the observed phenomena.

We might view motivation or counseling as the intervening variable in the work-status and breastfeeding relationship.

Thus, motive, job satisfaction, responsibility, behavior, and justice are some of the examples of intervening variables.

Suppressor Variable

In many cases, we have good reasons to believe that the variables of interest have a relationship, but our data fail to establish any such relationship. Some hidden factors may suppress the true relationship between the two original variables.

Such a factor is referred to as a suppressor variable because it suppresses the relationship between the other two variables.

The suppressor variable suppresses the relationship by being positively correlated with one of the variables in the relationship and negatively correlated with the other. The true relationship between the two variables will reappear when the suppressor variable is controlled for.

Thus, for example, low age may pull education up but income down. In contrast, a high age may pull income up but education down, effectively canceling the relationship between education and income unless age is controlled for.

4 Relationships Between Variables

Variables: Definition, Examples, Types Of Variables In Research

In dealing with relationships between variables in research, we observe a variety of dimensions in these relationships.

Positive and Negative Relationship

Symmetrical relationship, causal relationship, linear and non-linear relationship.

Two or more variables may have a positive, negative, or no relationship. In the case of two variables, a positive relationship is one in which both variables vary in the same direction.

However, they are said to have a negative relationship when they vary in opposite directions.

When a change in the other variable does not accompany the change or movement of one variable, we say that the variables in question are unrelated.

For example, if an increase in wage rate accompanies one’s job experience, the relationship between job experience and the wage rate is positive.

If an increase in an individual’s education level decreases his desire for additional children, the relationship is negative or inverse.

If the level of education does not have any bearing on the desire, we say that the variables’ desire for additional children and ‘education’ are unrelated.

Strength of Relationship

Once it has been established that two variables are related, we want to ascertain how strongly they are related.

A common statistic to measure the strength of a relationship is the so-called correlation coefficient symbolized by r. r is a unit-free measure, lying between -1 and +1 inclusive, with zero signifying no linear relationship.

As far as the prediction of one variable from the knowledge of the other variable is concerned, a value of r= +1 means a 100% accuracy in predicting a positive relationship between the two variables, and a value of r = -1 means a 100% accuracy in predicting a negative relationship between the two variables.

So far, we have discussed only symmetrical relationships in which a change in the other variable accompanies a change in either variable.

This relationship does not indicate which variable is the independent variable and which variable is the dependent variable.

In other words, you can label either of the variables as the independent variable.

Such a relationship is a symmetrical  relationship. In an asymmetrical relationship, a change in variable X (say) is accompanied by a change in variable Y, but not vice versa.

The amount of rainfall, for example, will increase productivity , but productivity will not affect the rainfall. This is an asymmetrical relationship.

Similarly, the relationship between smoking and lung cancer would be asymmetrical because smoking could cause cancer, but lung cancer could not cause smoking.

Indicating a relationship between two variables does not automatically ensure that changes in one variable cause changes in another.

It is, however, very difficult to establish the existence of causality between variables. While no one can ever be certain that variable A causes variable B , one can gather some evidence that increases our belief that A leads to B.

In an attempt to do so, we seek the following evidence:

  • Is there a relationship between A and B?  When such evidence exists, it indicates a possible causal link between the variables.
  • Is the relationship asymmetrical so that a change in A results in B but not vice-versa? In other words, does A occur before B? If we find that B occurs before A, we can have little confidence that A causes.
  • Does a change in A result in a change in B regardless of the actions of other factors? Or, is it possible to eliminate other possible causes of B? Can one determine that C, D, and E (say) do not co-vary with B in a way that suggests possible causal connections?

A linear relationship is a straight-line relationship between two variables, where the variables vary at the same rate regardless of whether the values are low, high, or intermediate.

This is in contrast with the non-linear (or curvilinear) relationships, where the rate at which one variable changes in value may differ for different values of the second variable.

Whether a variable is linearly related to the other variable or not can simply be ascertained by plotting the K values against X values.

If the values, when plotted, appear to lie on a straight line, the existence of a linear relationship between X and Y is suggested.

Height and weight almost always have an approximately linear relationship, while age and fertility rates have a non-linear relationship.

Frequently Asked Questions about Variable

What is a variable within the context of a research investigation.

A variable, within the context of a research investigation, refers to concepts that vary. It can be any property, characteristic, number, or quantity that can increase or decrease over time or take on different values.

How is a variable used in research?

In research, a variable is any property or characteristic that can take on different values. Experiments often manipulate variables to compare outcomes. For instance, an experimenter might compare the effectiveness of different types of fertilizers, where the variable is the ‘type of fertilizers.’

What distinguishes qualitative variables from quantitative variables?

Qualitative variables express a qualitative attribute, such as hair color or religion, and do not imply a meaningful numerical ordering. Quantitative variables, on the other hand, are measured in terms of numbers, like a person’s age or family size.

How do discrete and continuous variables differ in terms of quantitative variables?

Discrete variables are restricted to certain values, often whole numbers, resulting from enumeration or counting, like the number of children in a household. Continuous variables can take on an infinite number of intermediate values along a specified interval, such as the time required to complete a test.

What are the roles of independent and dependent variables in research?

In research, the independent variable is manipulated by the researcher to observe its effects on the dependent variable. The independent variable is the presumed cause or influence, while the dependent variable is the outcome or effect that is being measured.

What is a background variable in a study?

Background variables are information collected in a study, such as age, sex, or educational attainment. These variables are often related to many independent variables and indirectly influence the main problem or outcome, hence they are termed background variables.

How does a suppressor variable affect the relationship between two other variables?

A suppressor variable can suppress or hide the true relationship between two other variables. It does this by being positively correlated with one of the variables and negatively correlated with the other. When the suppressor variable is controlled for, the true relationship between the two original variables can be observed.

Now that you are familiar with variables; explore complete guideline on research and research methodology concepts .

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Home » ANOVA (Analysis of variance) – Formulas, Types, and Examples

ANOVA (Analysis of variance) – Formulas, Types, and Examples

Table of Contents

ANOVA

Analysis of Variance (ANOVA)

Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means. It is similar to the t-test, but the t-test is generally used for comparing two means, while ANOVA is used when you have more than two means to compare.

ANOVA is based on comparing the variance (or variation) between the data samples to the variation within each particular sample. If the between-group variance is high and the within-group variance is low, this provides evidence that the means of the groups are significantly different.

ANOVA Terminology

When discussing ANOVA, there are several key terms to understand:

  • Factor : This is another term for the independent variable in your analysis. In a one-way ANOVA, there is one factor, while in a two-way ANOVA, there are two factors.
  • Levels : These are the different groups or categories within a factor. For example, if the factor is ‘diet’ the levels might be ‘low fat’, ‘medium fat’, and ‘high fat’.
  • Response Variable : This is the dependent variable or the outcome that you are measuring.
  • Within-group Variance : This is the variance or spread of scores within each level of your factor.
  • Between-group Variance : This is the variance or spread of scores between the different levels of your factor.
  • Grand Mean : This is the overall mean when you consider all the data together, regardless of the factor level.
  • Treatment Sums of Squares (SS) : This represents the between-group variability. It is the sum of the squared differences between the group means and the grand mean.
  • Error Sums of Squares (SS) : This represents the within-group variability. It’s the sum of the squared differences between each observation and its group mean.
  • Total Sums of Squares (SS) : This is the sum of the Treatment SS and the Error SS. It represents the total variability in the data.
  • Degrees of Freedom (df) : The degrees of freedom are the number of values that have the freedom to vary when computing a statistic. For example, if you have ‘n’ observations in one group, then the degrees of freedom for that group is ‘n-1’.
  • Mean Square (MS) : Mean Square is the average squared deviation and is calculated by dividing the sum of squares by the corresponding degrees of freedom.
  • F-Ratio : This is the test statistic for ANOVAs, and it’s the ratio of the between-group variance to the within-group variance. If the between-group variance is significantly larger than the within-group variance, the F-ratio will be large and likely significant.
  • Null Hypothesis (H0) : This is the hypothesis that there is no difference between the group means.
  • Alternative Hypothesis (H1) : This is the hypothesis that there is a difference between at least two of the group means.
  • p-value : This is the probability of obtaining a test statistic as extreme as the one that was actually observed, assuming that the null hypothesis is true. If the p-value is less than the significance level (usually 0.05), then the null hypothesis is rejected in favor of the alternative hypothesis.
  • Post-hoc tests : These are follow-up tests conducted after an ANOVA when the null hypothesis is rejected, to determine which specific groups’ means (levels) are different from each other. Examples include Tukey’s HSD, Scheffe, Bonferroni, among others.

Types of ANOVA

Types of ANOVA are as follows:

One-way (or one-factor) ANOVA

This is the simplest type of ANOVA, which involves one independent variable . For example, comparing the effect of different types of diet (vegetarian, pescatarian, omnivore) on cholesterol level.

Two-way (or two-factor) ANOVA

This involves two independent variables. This allows for testing the effect of each independent variable on the dependent variable , as well as testing if there’s an interaction effect between the independent variables on the dependent variable.

Repeated Measures ANOVA

This is used when the same subjects are measured multiple times under different conditions, or at different points in time. This type of ANOVA is often used in longitudinal studies.

Mixed Design ANOVA

This combines features of both between-subjects (independent groups) and within-subjects (repeated measures) designs. In this model, one factor is a between-subjects variable and the other is a within-subjects variable.

Multivariate Analysis of Variance (MANOVA)

This is used when there are two or more dependent variables. It tests whether changes in the independent variable(s) correspond to changes in the dependent variables.

Analysis of Covariance (ANCOVA)

This combines ANOVA and regression. ANCOVA tests whether certain factors have an effect on the outcome variable after removing the variance for which quantitative covariates (interval variables) account. This allows the comparison of one variable outcome between groups, while statistically controlling for the effect of other continuous variables that are not of primary interest.

Nested ANOVA

This model is used when the groups can be clustered into categories. For example, if you were comparing students’ performance from different classrooms and different schools, “classroom” could be nested within “school.”

ANOVA Formulas

ANOVA Formulas are as follows:

Sum of Squares Total (SST)

This represents the total variability in the data. It is the sum of the squared differences between each observation and the overall mean.

  • yi represents each individual data point
  • y_mean represents the grand mean (mean of all observations)

Sum of Squares Within (SSW)

This represents the variability within each group or factor level. It is the sum of the squared differences between each observation and its group mean.

  • yij represents each individual data point within a group
  • y_meani represents the mean of the ith group

Sum of Squares Between (SSB)

This represents the variability between the groups. It is the sum of the squared differences between the group means and the grand mean, multiplied by the number of observations in each group.

  • ni represents the number of observations in each group
  • y_mean represents the grand mean

Degrees of Freedom

The degrees of freedom are the number of values that have the freedom to vary when calculating a statistic.

For within groups (dfW):

For between groups (dfB):

For total (dfT):

  • N represents the total number of observations
  • k represents the number of groups

Mean Squares

Mean squares are the sum of squares divided by the respective degrees of freedom.

Mean Squares Between (MSB):

Mean Squares Within (MSW):

F-Statistic

The F-statistic is used to test whether the variability between the groups is significantly greater than the variability within the groups.

If the F-statistic is significantly higher than what would be expected by chance, we reject the null hypothesis that all group means are equal.

Examples of ANOVA

Examples 1:

Suppose a psychologist wants to test the effect of three different types of exercise (yoga, aerobic exercise, and weight training) on stress reduction. The dependent variable is the stress level, which can be measured using a stress rating scale.

Here are hypothetical stress ratings for a group of participants after they followed each of the exercise regimes for a period:

  • Yoga: [3, 2, 2, 1, 2, 2, 3, 2, 1, 2]
  • Aerobic Exercise: [2, 3, 3, 2, 3, 2, 3, 3, 2, 2]
  • Weight Training: [4, 4, 5, 5, 4, 5, 4, 5, 4, 5]

The psychologist wants to determine if there is a statistically significant difference in stress levels between these different types of exercise.

To conduct the ANOVA:

1. State the hypotheses:

  • Null Hypothesis (H0): There is no difference in mean stress levels between the three types of exercise.
  • Alternative Hypothesis (H1): There is a difference in mean stress levels between at least two of the types of exercise.

2. Calculate the ANOVA statistics:

  • Compute the Sum of Squares Between (SSB), Sum of Squares Within (SSW), and Sum of Squares Total (SST).
  • Calculate the Degrees of Freedom (dfB, dfW, dfT).
  • Calculate the Mean Squares Between (MSB) and Mean Squares Within (MSW).
  • Compute the F-statistic (F = MSB / MSW).

3. Check the p-value associated with the calculated F-statistic.

  • If the p-value is less than the chosen significance level (often 0.05), then we reject the null hypothesis in favor of the alternative hypothesis. This suggests there is a statistically significant difference in mean stress levels between the three exercise types.

4. Post-hoc tests

  • If we reject the null hypothesis, we conduct a post-hoc test to determine which specific groups’ means (exercise types) are different from each other.

Examples 2:

Suppose an agricultural scientist wants to compare the yield of three varieties of wheat. The scientist randomly selects four fields for each variety and plants them. After harvest, the yield from each field is measured in bushels. Here are the hypothetical yields:

The scientist wants to know if the differences in yields are due to the different varieties or just random variation.

Here’s how to apply the one-way ANOVA to this situation:

  • Null Hypothesis (H0): The means of the three populations are equal.
  • Alternative Hypothesis (H1): At least one population mean is different.
  • Calculate the Degrees of Freedom (dfB for between groups, dfW for within groups, dfT for total).
  • If the p-value is less than the chosen significance level (often 0.05), then we reject the null hypothesis in favor of the alternative hypothesis. This would suggest there is a statistically significant difference in mean yields among the three varieties.
  • If we reject the null hypothesis, we conduct a post-hoc test to determine which specific groups’ means (wheat varieties) are different from each other.

How to Conduct ANOVA

Conducting an Analysis of Variance (ANOVA) involves several steps. Here’s a general guideline on how to perform it:

  • Null Hypothesis (H0): The means of all groups are equal.
  • Alternative Hypothesis (H1): At least one group mean is different from the others.
  • The significance level (often denoted as α) is usually set at 0.05. This implies that you are willing to accept a 5% chance that you are wrong in rejecting the null hypothesis.
  • Data should be collected for each group under study. Make sure that the data meet the assumptions of an ANOVA: normality, independence, and homogeneity of variances.
  • Calculate the Degrees of Freedom (df) for each sum of squares (dfB, dfW, dfT).
  • Compute the Mean Squares Between (MSB) and Mean Squares Within (MSW) by dividing the sum of squares by the corresponding degrees of freedom.
  • Compute the F-statistic as the ratio of MSB to MSW.
  • Determine the critical F-value from the F-distribution table using dfB and dfW.
  • If the calculated F-statistic is greater than the critical F-value, reject the null hypothesis.
  • If the p-value associated with the calculated F-statistic is smaller than the significance level (0.05 typically), you reject the null hypothesis.
  • If you rejected the null hypothesis, you can conduct post-hoc tests (like Tukey’s HSD) to determine which specific groups’ means (if you have more than two groups) are different from each other.
  • Regardless of the result, report your findings in a clear, understandable manner. This typically includes reporting the test statistic, p-value, and whether the null hypothesis was rejected.

When to use ANOVA

ANOVA (Analysis of Variance) is used when you have three or more groups and you want to compare their means to see if they are significantly different from each other. It is a statistical method that is used in a variety of research scenarios. Here are some examples of when you might use ANOVA:

  • Comparing Groups : If you want to compare the performance of more than two groups, for example, testing the effectiveness of different teaching methods on student performance.
  • Evaluating Interactions : In a two-way or factorial ANOVA, you can test for an interaction effect. This means you are not only interested in the effect of each individual factor, but also whether the effect of one factor depends on the level of another factor.
  • Repeated Measures : If you have measured the same subjects under different conditions or at different time points, you can use repeated measures ANOVA to compare the means of these repeated measures while accounting for the correlation between measures from the same subject.
  • Experimental Designs : ANOVA is often used in experimental research designs when subjects are randomly assigned to different conditions and the goal is to compare the means of the conditions.

Here are the assumptions that must be met to use ANOVA:

  • Normality : The data should be approximately normally distributed.
  • Homogeneity of Variances : The variances of the groups you are comparing should be roughly equal. This assumption can be tested using Levene’s test or Bartlett’s test.
  • Independence : The observations should be independent of each other. This assumption is met if the data is collected appropriately with no related groups (e.g., twins, matched pairs, repeated measures).

Applications of ANOVA

The Analysis of Variance (ANOVA) is a powerful statistical technique that is used widely across various fields and industries. Here are some of its key applications:

Agriculture

ANOVA is commonly used in agricultural research to compare the effectiveness of different types of fertilizers, crop varieties, or farming methods. For example, an agricultural researcher could use ANOVA to determine if there are significant differences in the yields of several varieties of wheat under the same conditions.

Manufacturing and Quality Control

ANOVA is used to determine if different manufacturing processes or machines produce different levels of product quality. For instance, an engineer might use it to test whether there are differences in the strength of a product based on the machine that produced it.

Marketing Research

Marketers often use ANOVA to test the effectiveness of different advertising strategies. For example, a marketer could use ANOVA to determine whether different marketing messages have a significant impact on consumer purchase intentions.

Healthcare and Medicine

In medical research, ANOVA can be used to compare the effectiveness of different treatments or drugs. For example, a medical researcher could use ANOVA to test whether there are significant differences in recovery times for patients who receive different types of therapy.

ANOVA is used in educational research to compare the effectiveness of different teaching methods or educational interventions. For example, an educator could use it to test whether students perform significantly differently when taught with different teaching methods.

Psychology and Social Sciences

Psychologists and social scientists use ANOVA to compare group means on various psychological and social variables. For example, a psychologist could use it to determine if there are significant differences in stress levels among individuals in different occupations.

Biology and Environmental Sciences

Biologists and environmental scientists use ANOVA to compare different biological and environmental conditions. For example, an environmental scientist could use it to determine if there are significant differences in the levels of a pollutant in different bodies of water.

Advantages of ANOVA

Here are some advantages of using ANOVA:

Comparing Multiple Groups: One of the key advantages of ANOVA is the ability to compare the means of three or more groups. This makes it more powerful and flexible than the t-test, which is limited to comparing only two groups.

Control of Type I Error: When comparing multiple groups, the chances of making a Type I error (false positive) increases. One of the strengths of ANOVA is that it controls the Type I error rate across all comparisons. This is in contrast to performing multiple pairwise t-tests which can inflate the Type I error rate.

Testing Interactions: In factorial ANOVA, you can test not only the main effect of each factor, but also the interaction effect between factors. This can provide valuable insights into how different factors or variables interact with each other.

Handling Continuous and Categorical Variables: ANOVA can handle both continuous and categorical variables . The dependent variable is continuous and the independent variables are categorical.

Robustness: ANOVA is considered robust to violations of normality assumption when group sizes are equal. This means that even if your data do not perfectly meet the normality assumption, you might still get valid results.

Provides Detailed Analysis: ANOVA provides a detailed breakdown of variances and interactions between variables which can be useful in understanding the underlying factors affecting the outcome.

Capability to Handle Complex Experimental Designs: Advanced types of ANOVA (like repeated measures ANOVA, MANOVA, etc.) can handle more complex experimental designs, including those where measurements are taken on the same subjects over time, or when you want to analyze multiple dependent variables at once.

Disadvantages of ANOVA

Some limitations or disadvantages that are important to consider:

Assumptions: ANOVA relies on several assumptions including normality (the data follows a normal distribution), independence (the observations are independent of each other), and homogeneity of variances (the variances of the groups are roughly equal). If these assumptions are violated, the results of the ANOVA may not be valid.

Sensitivity to Outliers: ANOVA can be sensitive to outliers. A single extreme value in one group can affect the sum of squares and consequently influence the F-statistic and the overall result of the test.

Dichotomous Variables: ANOVA is not suitable for dichotomous variables (variables that can take only two values, like yes/no or male/female). It is used to compare the means of groups for a continuous dependent variable.

Lack of Specificity: Although ANOVA can tell you that there is a significant difference between groups, it doesn’t tell you which specific groups are significantly different from each other. You need to carry out further post-hoc tests (like Tukey’s HSD or Bonferroni) for these pairwise comparisons.

Complexity with Multiple Factors: When dealing with multiple factors and interactions in factorial ANOVA, interpretation can become complex. The presence of interaction effects can make main effects difficult to interpret.

Requires Larger Sample Sizes: To detect an effect of a certain size, ANOVA generally requires larger sample sizes than a t-test.

Equal Group Sizes: While not always a strict requirement, ANOVA is most powerful and its assumptions are most likely to be met when groups are of equal or similar sizes.

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27 Types of Variables in Research and Statistics

types of variables in research, explained below

In research and statistics, a variable is a characteristic or attribute that can take on different values or categories. It represents data points or information that can be measured, observed, or manipulated within a study.

Statistical and experimental analysis aims to explore the relationships between variables. For example, researchers may hypothesize a connection between a particular variable and an outcome, like the association between physical activity levels (an independent variable) and heart health (a dependent variable).

Variables play a crucial role in data analysis . Data sets collected through research typically consist of multiple variables, and the analysis is driven by how these variables are related, how they influence each other, and what patterns emerge from these relationships.

Therefore, as a researcher, your understanding of variables and their manipulation forms the crux of your study.

To help with your understanding, I’ve presented 27 of the most common types of variables below.

Types of Variables

1. quantitative (numerical) variables.

Definition: Quantitative variables, also known as numerical variables, are quantifiable in nature and represented in numbers, allowing the data collected to be measured on a scale or range (Moodie & Johnson, 2021). These variables generally yield data that can be organized, ranked, measured, and subjected to mathematical operations.

Explanation: The values of quantitative variables can either be counted (referred to as discrete variables) or measured (continuous variables). Quantifying data in numerical form allows for a range of statistical analysis techniques to be applied, from calculating averages to finding correlations.

Quantitative Variable Example : Consider a marketing survey where you ask respondents to rate their satisfaction with your product on a scale of 1 to 10. The satisfaction score here represents a quantitative variable. The data can be quantified and used to calculate average satisfaction scores, identify the scope for product improvement, or compare satisfaction levels across different demographic groups.

2. Continuous Variables

Definition: Continuous variables are a subtype of quantitative variables that can have an infinite number of measurements within a specified range. They provide detailed insights based on precise measurements and are often representative on a continuous scale (Christmann & Badgett, 2009).

Explanation: The variable is “continuous” because there are an infinite number of possible values within the chosen range. For instance, variables like height, weight, or time are measured continuously.

Continuous Variable Example : The best real-world example of a continuous variable is time. For instance, the time it takes for a customer service representative to resolve a customer issue can range anywhere from few seconds to several hours, and can accurately be measured down to the second, providing an almost finite set of possible values.

3. Discrete Variables

Definition: Discrete variables are a form of quantitative variable that can only assume a finite number of values. They are typically count-based (Frankfort-Nachmias & Leon-Guerrero, 2006).

Explanation: Discrete variables are commonly used in situations where the “count” or “quantity” is distinctly separate. For instance, the number of children in a family is a common example – you can’t have 2.5 kids.

Discrete Variable Example : The number of times a customer contacts customer service within a month. This is a discrete variable because it can only take a whole number of values – you can’t call customer service 2.5 times.

4. Qualitative (Categorical) Variables

Definition: Qualitative, or categorical variables, are non-numerical data points that categorize or group data entities based on shared features or qualities (Moodie & Johnson, 2021).

Explanation: They are often used in research to classify particular traits, characteristics, or properties of subjects that are not easily quantifiable, such as colors, textures, tastes, or smells.

Qualitative Variable Example : Consider a survey that asks respondents to identify their favorite color from a list of choices. The color preference would be a qualitative variable as it categorizes data into different categories corresponding to different colors.

5. Nominal Variables

Definition: Nominal variables, a subtype of qualitative variables, represent categories without any inherent order or ranking (Norman & Streiner, 2008).

Explanation: Nominal variables are often used to label or categorize particular sets of items or individuals, with no intention of giving numerical value or order. For example, race, gender, or religion.

Nominal Variable Example : For instance, the type of car someone owns (sedan, SUV, truck, etc.) is a nominal variable. Each category is unique and one is not inherently higher, better, or larger than the others.

6. Ordinal Variables

Definition: Ordinal variables are a subtype of categorical (qualitative) variables with a key feature of having a clear, distinct, and meaningful order or ranking to the categories (De Vaus, 2001).

Explanation: Ordinal variables represent categories that can be logically arranged in a specific order or sequence but the difference between categories is unknown or doesn’t matter, such as satisfaction rating scale (unsatisfied, neutral, satisfied).

Ordinal Variable Example : A classic example is asking survey respondents how strongly they agree or disagree with a statement (strongly disagree, disagree, neither agree nor disagree, agree, strongly agree). The answers form an ordinal scale; they can be ranked, but the intervals between responses are not necessarily equal.

7. Dichotomous (Binary) Variables

Definition: Dichotomous or binary variables are a type of categorical variable that consist of only two opposing categories like true/false, yes/no, success/failure, and so on (Adams & McGuire, 2022).

Explanation: Dichotomous variables refer to situations where there can only be two, and just two, possible outcomes – there is no middle ground.

Dichotomous Variable Example : Whether a customer completed a transaction (Yes or No) is a binary variable. Either they completed the purchase (yes) or they did not (no).

8. Ratio Variables

Definition: Ratio variables are the highest level of quantitative variables that contain a zero point or absolute zero, which represents a complete absence of the quantity (Norman & Streiner, 2008).

Explanation: Besides being able to categorize and order units, ratio variables also allow for the relative degree of difference between them to be calculated. For example, income, height, weight, and temperature (in Kelvin) are ratio variables.

Ratio Variable Example : An individual’s annual income is a ratio variable. You can say someone earning $50,000 earns twice as much as someone making $25,000. The zero point in this case would be an income of $0, which indicates that no income is being earned.

9. Interval Variables

Definition: Interval variables are quantitative variables that have equal, predictable differences between values, but they do not have a true zero point (Norman & Streiner, 2008).

Explanation: Interval variables are similar to ratio variables; both provide a clear ordering of categories and have equal intervals between successive values. The primary difference is the absence of an absolute zero.

Interval Variable Example : The classic example of an interval variable is the temperature in Fahrenheit or Celsius. The difference between 20 degrees and 30 degrees is the same as the difference between 70 degrees and 80 degrees, but there isn’t a true zero because the scale doesn’t start from absolute nonexistence of the quantity being measured.

Related: Quantitative Reasoning Examples

10. Dependent Variables

Definition: The dependent variable is the outcome or effect that the researcher wants to study. Its value depends on or is influenced by one or more other variables known as independent variables.

Explanation: In a research study, the dependent variable is the phenomenon or behavior that may be affected by manipulations in the independent variable. It’s what you measure to see if your predictions about the effects of the independent variable are correct.

Dependent Variable Example: Suppose you want to study the impact of exercise frequency on weight loss. In this case, the dependent variable is weight loss, which changes based on how often the subject exercises (the independent variable).

11. Independent Variables

Definition: The independent variable, or the predictor variable, is what the researcher manipulates to test its effect on the dependent variable.

Explanation: The independent variable is presumed to have some effect on the dependent variable in a study. It can often be thought of as the cause in a cause-and-effect relationship.

Independent Variable Example: In a study looking at how different dosages of a medication affect the severity of symptoms, the medication dosage is an independent variable. Researchers will adjust the dosage to see what effect it has on the symptoms (the dependent variable).

See Also: Independent and Dependent Variable Examples

12. Confounding Variables

Definition: Confounding variables—also known as confounders—are variables that might distort, confuse or interfere with the relationship between an independent variable and a dependent variable, leading to a false correlation (Boniface, 2019).

Explanation: Confounders are typically related in some way to both the independent and dependent variables. Because of this, they can create or hide relationships, leading researchers to make inaccurate conclusions about causality.

Confounding Variable Example : If you’re studying the relationship between physical activity and heart health, diet could potentially act as a confounding variable. People who are physically active often also eat healthier diets, which could independently improve heart health [National Heart, Lung, and Blood Institute].

13. Control Variables

Definition: Control variables are variables in a research study that the researcher keeps constant to prevent them from interfering with the relationship between the independent and dependent variables (Sproull, 2002).

Explanation: Control variables allow researchers to isolate the effects of the independent variable on the dependent variable, ensuring that any changes observed are solely due to the manipulation of the independent variable and not an external factor.

Control Variable Example : In a study evaluating the impact of a tutoring program on student performance, some control variables could include the teacher’s experience, the type of test used to measure performance, and the student’s previous grades.

14. Latent Variables

Definition: Latent variables—also referred to as hidden or unobserved variables—are variables that are not directly observed or measured but are inferred from other variables that are observed (measured directly).

Explanation: Latent variables can represent abstract concepts like intelligence, socioeconomic status, or even happiness. They are often used in psychological and sociological research, where certain concepts can’t be measured directly.

Latent Variable Example: In a study on job satisfaction, factors like job stress, financial reward, work-life balance, or relationship with colleagues can be measured directly. However, “job satisfaction” itself is a latent variable as it is inferred from these observed variables.

15. Derived Variables

Definition: Derived variables are variables that are created or developed based on existing variables in a dataset. They involve applying certain calculations or manipulations to one or more variables to create a new one.

Explanation: Derived variables can be created by either transforming a single variable (like taking the square root) or combining multiple variables (computing the ratio of two variables).

Derived Variable Example: In a dataset containing a person’s height and weight, a derived variable could be the Body Mass Index (BMI). The BMI is calculated by dividing weight (in kilograms) by the square of height (in meters).

16. Time-series Variables

Definition: Time-series variables are a set of data points ordered or indexed in time order. They provide a sequence of data points, each associated with a specific instance in time.

Explanation: Time-series variables are often used in statistical models to study trends, analyze patterns over time, make forecasts, and understand underlying causes and characteristics of the trend.

Time-series Variable Example : The quarterly GDP (Gross Domestic Product) data over a period of several years would be an example of a time series variable. Economists use such data to examine economic trends over time.

17. Cross-sectional Variables

Definition: Cross-sectional variables are data collected from many subjects at the same point in time or without regard to differences in time.

Explanation: This type of data provides a “snapshot” of the variables at a specific time. They’re often used in research to compare different population groups at a single point in time.

Cross-sectional Variable Example: A basic example of a set of cross-sectional data could be a national survey that asks respondents about their current employment status. The data captured represents a single point in time and does not track changes in employment over time.

18. Predictor Variables

Definition: A predictor variable—also known as independent or explanatory variable—is a variable that is being manipulated in an experiment or study to see how it influences the dependent or response variable.

Explanation: In a cause-and-effect relationship, the predictor variable is the cause. Its modification allows the researcher to study its effect on the response variable.

Predictor Variable Example : In a study evaluating the impact of studying hours on exam score, the number of studying hours is a predictor variable. Researchers alter the study duration to see its impact on the exam results (response variable).

19. Response Variables

Definition: A response variable—also known as the dependent or outcome variable—is what the researcher observes for any changes in an experiment or study. Its value depends on the predictor or independent variable.

Explanation: The response variable is the “effect” in a cause-and-effect scenario. Any changes occurring to this variable due to the predictor variable are observed and recorded.

Response Variable Example: Continuing from the previous example, the exam score is the response variable. It changes based on the manipulation of the predictor variable, i.e., the number of studying hours.

20. Exogenous Variables

Definition: Exogenous variables are variables that are not affected by other variables in the system but can affect other variables within the same system.

Explanation: In a model, an exogenous variable is considered to be an input, it’s determined outside the model, and its value is simply imposed on the system.

Exogenous Variable Example: In an economic model, the government’s taxation rate may be considered an exogenous variable. The rate is set externally (not determined within the economic model) but impacts variables within the model, such as business profitability.

21. Endogenous Variables

Definition: In contrast, endogenous variables are variables whose value is determined by the functional relationships within the system in an economic or statistical model. They depend on the values of other variables in the model.

Explanation: These are the “output” variables of a system, determined through cause-and-effect relationships within the system.

Endogenous Variable Example: To continue the previous example, business profitability in an economic model may be considered an endogenous variable. It is influenced by several other variables within the model, including the exogenous taxation rate set by the government.

22. Causal Variables

Definition: Causal variables are variables which can directly cause an effect on the outcome or dependent variable. Their value or level determines the value or level of other variables.

Explanation: In a cause-and-effect relationship, a causal variable is the cause. The understanding of causal relationships is the basis of scientific enquiry, allowing researchers to manipulate variables to see the effect.

Causal Variable Example: In a study examining the effect of fertilizer on plant growth, the type or amount of fertilizer used is the causal variable. Changing its type or amount should directly affect the outcome—plant growth.

23. Moderator Variables

Definition: Moderator variables are variables that can affect the strength or direction of the association between the predictor (independent) and response (dependent) variable. They specify when or under what conditions a relationship holds.

Explanation: The role of a moderator is to illustrate “how” or “when” an independent variable’s effect on a dependent variable changes.

Moderator Variable Example: If you are studying the effect of a training program on job performance, a potential moderator variable could be the employee’s education level. The influence of the training program on job performance could depend on the employee’s initial level of education.

24. Mediator Variables

Definition: Mediator variables are variables that account for, or explain, the relationship between an independent variable and a dependent variable, providing an understanding of “why” or “how” an effect occurs.

Explanation: Often, the relationship between an independent and a dependent variable isn’t direct—it’s through a third, intervening, variable known as a mediator variable.

Mediator Variable Example: In a study looking at the relationship between socioeconomic status and academic performance, a mediator variable might be the access to educational resources. Socioeconomic status may influence access to educational resources, which in turn affects academic performance. The relationship between socioeconomic status and academic performance isn’t direct but through access to resources.

25. Extraneous Variables

Definition: Extraneous variables are variables that are not of primary interest to a researcher but might influence the outcome of a study. They can add “noise” to the research data if not controlled.

Explanation: An extraneous variable is anything else that has the potential to influence our dependent variable or confound our results if not kept in check, other than our independent variable.

Extraneous Variable Example : Consider an experiment to test whether temperature influences the rate of a chemical reaction. Potential extraneous variables could include the light level, humidity, or impurities in the chemicals used—each could affect the reaction rate and, thus, should be controlled to ensure valid results.

26. Dummy Variables

Definition: Dummy variables, often used in regression analysis, are artificial variables created to represent an attribute with two or more distinct categories or levels.

Explanation: They are used to turn a qualitative variable into a quantitative one to facilitate mathematical processing. Typically, dummy variables are binary – taking a value of either 0 or 1.

Dummy Variable Example: Consider a dataset that includes a variable “Gender” with categories “male” and “female”. A corresponding dummy variable “IsMale” could be introduced, where males get classified as 1 and females as 0.

27. Composite Variables

Definition: Composite variables are new variables created by combining or grouping two or more variables.

Explanation: Depending upon their complexity, composite variables can help assess concepts that are explicit (e.g., “total score”) or relatively abstract (e.g., “life quality index”).

Composite Variable Example: A “Healthy Living Index” might be created as a composite of multiple variables such as eating habits, physical activity level, sleep quality, and stress level. Each of these variables contributes to the overall “Healthy Living Index”.

Knowing your variables will make you a better researcher. Some you need to keep an eye out for: confounding variables , for instance, always need to be in the backs of our minds. Others you need to think about during study design, matching the research design to the research objectives.

Adams, K. A., & McGuire, E. K. (2022). Research Methods, Statistics, and Applications . SAGE Publications.

Allen, M. (2017). The SAGE Encyclopedia of Communication Research Methods (Vol. 1). New York: SAGE Publications.

Babbie, E., Halley, F., & Zaino, J. (2007).  Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows  (6th ed.). New York: SAGE Publications.

Boniface, D. R. (2019). Experiment Design and Statistical Methods For Behavioural and Social Research . CRC Press. ISBN: 9781351449298.

Christmann, E. P., & Badgett, J. L. (2009). Interpreting Assessment Data: Statistical Techniques You Can Use. New York: NSTA Press.

Coolidge, F. L. (2012). Statistics: A Gentle Introduction (3rd ed.). SAGE Publications.

Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . New York: SAGE Publications.

De Vaus, D. A. (2001). Research Design in Social Research . New York: SAGE Publications.

Katz, M. (2006) . Study Design and Statistical Analysis: A Practical Guide for Clinicians . Cambridge: Cambridge University Press.

Knapp, H. (2017). Intermediate Statistics Using SPSS. SAGE Publications.

Moodie, P. F., & Johnson, D. E. (2021). Applied Regression and ANOVA Using SAS. CRC Press.

Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials . New York: B.C. Decker.

Privitera, G. J. (2022). Research Methods for the Behavioral Sciences . New Jersey: SAGE Publications.

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Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

  • Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 41 Important Classroom Expectations (for This School Year)
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Big data research in nursing: A bibliometric exploration of themes and publications

Affiliations.

  • 1 Department of Emergency Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • 2 School of Artificial Intelligence, Shenyang University of Technology, Shenyang, China.
  • PMID: 38140780
  • DOI: 10.1111/jnu.12954

Aims: To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain.

Design: Bibliometric analysis.

Methods: The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer.

Results: The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field.

Conclusions: The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field.

Clinical relevance: This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

Keywords: bibliometrics; big data; nursing; visual analysis.

© 2023 Sigma Theta Tau International.

Grants and funding

  • 22A320067/the Key Research Project in Higher Education in Henan, China
  • SBGJ202103076/Medical science and technology public relations project jointly built by Henan Health Commission
  • HLKY2023002/Nursing research Special Fund of the First Affiliated Hospital of Zhengzhou University

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Quantum Physics

Title: the security analysis of continuous-variable quantum key distribution under limited eavesdropping with practical fiber.

Abstract: Research on optimal eavesdropping models under practical conditions will help to evaluate realistic risk when employing quantum key distribution (QKD) system for secure information transmission. Intuitively, fiber loss will lead to the optical energy leaking to the environment, rather than harvested by the eavesdropper, which also limits the eavesdropping ability while improving the QKD system performance in practical use. However, defining the optimal eavesdropping model in the presence of lossy fiber is difficult because the channel is beyond the control of legitimate partners and the leaked signal is undetectable. Here we investigate how the fiber loss influences the eavesdropping ability based on a teleportation-based collective attack model which requires two distant stations and a shared entanglement source. We find that if the distributed entanglement is limited due to the practical loss, the optimal attack occurs when the two teleportation stations are merged to one and placed close to the transmitter site, which performs similar to the entangling-cloning attack but with a reduced wiretapping ratio. Assuming Eve uses the best available hollow-core fiber, the secret key rate in the practical environment can be 20%~40% higher than that under ideal eavesdropping. While if the entanglement distillation technology is mature enough to provide high quality of distributed entanglement, the two teleportation stations should be distantly separated for better eavesdropping performance, where the eavesdropping can even approach the optimal collective attack. Under the current level of entanglement purification technology, the unavoidable fiber loss can still greatly limit the eavesdropping ability as well as enhance the secret key rate and transmission distance of the realistic system, which promotes the development of QKD systems in practical application scenarios.

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  • Published: 02 January 2024

Do we overlook predictive factors in Poseidon 1 patients? A retrospective analysis co-evaluating antral follicle counts & diameters

  • Gürkan Uncu   ORCID: orcid.org/0000-0001-7660-8344 1 ,
  • Kiper Aslan   ORCID: orcid.org/0000-0002-9277-7735 1 ,
  • Cihan Cakir   ORCID: orcid.org/0000-0002-8332-7353 2 ,
  • Berrin Avci   ORCID: orcid.org/0000-0001-8135-5468 2 ,
  • Isil Kasapoglu   ORCID: orcid.org/0000-0002-1953-2475 1 &
  • Carlo Alviggi   ORCID: orcid.org/0000-0001-9116-020X 3  

Journal of Ovarian Research volume  17 , Article number:  1 ( 2024 ) Cite this article

Metrics details

An unexpected impaired ovarian response pertains to an insufficient reaction to controlled ovarian hyperstimulation. This deficient reaction is identified by a reduced count of mature follicles and retrieved oocytes during an IVF cycle, potentially diminishing the likelihood of a successful pregnancy. This research seeks to examine whether the characteristics of antral follicles can serve as predictive indicators for the unexpected impaired ovarian response to controlled ovarian stimulation (COS).

This retrospective cohort study was conducted at a tertiary university hospital. The electronic database of the ART (assisted reproductive technologies) center was screened between the years 2012–2022. Infertile women under 35 years, with normal ovarian reserve [anti-Müllerian hormone (AMH) > 1.2 ng/ml, antral follicle count (AFC) > 5] who underwent their first controlled ovarian stimulation (COS) cycle were selected. Women with < 9 oocytes retrieved (group 1 of the Poseidon classification) constituted the group A, whereas those with ≥  9 oocytes severed as control (normo-responders) one (group B). Demographic, anthropometric and hormonal variables together with COS parameters of the two groups were compared.

The number of patients with < 9 oocytes (group A) was 404, and those with ≥  9 oocytes were 602 (group B). The mean age of the group A was significantly higher (30.1 + 2.9 vs. 29.4 + 2.9, p  = 0.01). Group A displayed lower AMH and AFC [with interquartile ranges (IQR); AMH 1.6 ng/ml (1-2.6) vs. 3.5 ng/ml (2.2–5.4) p  < 0.01, AFC 8 (6–12) vs. 12 (9–17), p  < 0.01]. The number of small antral follicles (2–5 mm) of the group A was significantly lower [6 (4–8) vs. 8 (6–12) p  < 0.01), while the larger follicles (5–10 mm) remained similar [3 (1–5) vs. 3(1–6) p  = 0.3] between the groups.

The propensity of low ovarian reserve and higher age are the main risk factors for the impaired ovarian response. The proportion of the small antral follicles may be a predictive factor for ovarian response to prevent unexpected poor results.

Despite the advanced technology in assisted reproductive techniques (ART) for years, it is still impossible to predict accurately the ovarian response in patients who undergo controlled ovarian stimulation (COS). Clinicians may still face poor ovarian response despite doing accordance with evidence algorithms. In this case, explaining why a poor response is obtained in a young patient with a normal ovarian reserve and who has been stimulated with the appropriate dose of gonadotropin can be challenging. This situation is called “unexpected poor ovarian response” (uPOR). The incidence of uPOR varies between 10 and 40% depending on the threshold of the collected oocytes (4 or 9) [ 1 , 2 , 3 ]. While the Bologna criteria are the most accepted among these poor ovarian response classifications, the POSEIDON classification has been published recently and used worldwide [ 4 , 5 , 6 ]. The Poseidon classification is consistent with data reported by Drakopoulos et al. [ 1 ] which identified different prognostic categories in terms of live birth rate according to the number of oocytes retrieved. These data described an oocyte retrieval between 10 and 15 as “optimal”, while a number ranging between 4 and 9 as “suboptimal”. Finally, a number of oocytes between 1 and 3 was considered as “poor”. According to the POSEIDON criteria, the patients who have a number of oocytes retrieved between 1 and 9 despite normal ovarian reserve [antral follicle count (AFC) > 5–7, anti-Müllerian hormone (AMH) > 1.2 ng/ml] represent the unexpected impaired ovarian responses (uIOR) and are classified as groups 1 and 2. More specifically, women younger than 35 years of age are classified as POSEIDON Group-1 whereas, among them, those having 1–3 and 4–9 oocytes retrieved are identified as subgroups 1a and 1b, respectively. On the other hand, women aged 35 years and older with similar outcomes are categorized as POSEIDON Group 2a and 2b. Recent evidence confirmed that Poseidon groups 1 and 2 have significantly lower live birth rate (LBR) when compared with women having more than 9 oocytes retrieved, confirming the capability of Poseidon classification to identifying different low prognosis segments [ 7 ]. The exact etiology of uIOR or POSEIDON-1 remains unclear. There are various blamed factors for this phenomenon, including polymorphisms of gonadotropins and their receptors, dietary habits, environmental variable and drug administration errors [ 8 , 9 , 10 ]. On the other hand, novel developed indexes like follicle output rate (FORT - The ratio the number of pre-ovulatory follicles (16–22 mm in diameter) x 100 to the number of pre-antral follicles (3–8 mm in diameter)) and follicle to oocyte index (FOI- The ratio of the total number of oocytes collected at the end of stimulation to the number of antral follicles available at the start of stimulation) aim to assess intracycle ovarian response [ 11 , 12 ] before oocyte pick up. However, none of these etiologies could be predicted before COS, and none of the developed methods could avoid uIOR. Therefore, studies mainly focused on what can be done after uIOR to prevent recurrent POR.

The present study aims to investigate any patient characteristics that could predict and avoid impaired ovarian response (Poseidon 1 profile) before COS.

Study design & ethical approval

This retrospective cohort study was conducted at a tertiary university hospital’s Assisted Reproductive Technologies (ART) center. The study protocol was approved by the clinical trials ethical committee of the university with the number 2023-13/13.

Patient selection

The ART center’s electronic database was screened between 2012 and 2022. Patients who were eligible for POSEIDON-1 criteria (age < 35 years, AMH > 1.2 ng/ml, AFC > 5) were selected from all COS cycles. Patients who underwent their first COS cycle were enrolled to avoid bias. Patients with body mass index (BMI) over 35 kg/m2 and oocyte cryopreservation cycles due to any malignity were excluded from the study.

Assessment of ovarian reserve

Antral follicle count.

In all patients, the evaluation of antral follicle count was conducted through transvaginal ultrasound. The follicular assessment, as per Broekmans’ recommendations [ 13 ], took place just before ovarian stimulation on the 2nd or 3rd day of the menstrual cycle. The entire ovaries underwent scanning, and follicles within the 2–9 mm range were tallied. Antral follicles were categorized into two subgroups based on their size: those between 2 and 5 mm were designated as AFCa, while follicles ranging from 5 to 10 mm were labeled as AFCb.

  • Anti-mullerian hormone

Blood samples were collected on any day of the menstrual cycle and Anti-Mullerian hormone was analyzed by “Beckman Coulter Access II” enzymatic-immunoassay. The detection limit of the test was ≤ 0.02 ng/mL.

COS-ICSI-ET protocol

The routine infertility work-up was applied to all patients. After the basal transvaginal ultrasound check, the COS was started on the second or third day of the menstrual cycle. The daily gonadotropin dosage was adjusted depending on the patient’s age, BMI, ovarian reserve, and previous COS history. Recombinant Follicle Stimulating Hormone (rFSH) was used for COS. The standard COS protocol was the flexible antagonist protocol with hCG trigger. The trigger was applied when at least three follicles reached 17–18 mm.

Interventions

Patients were divided into two groups. All the results were compared between these groups. Group A consisted of patients with lower than nine oocytes, and Group B patients with equal or higher than nine oocytes. Group A was divided in two subgroups as Poseidon 1a (< 4 oocytes) and Poseidon 1b (4–9 oocytes). Demographic parameters (age, BMI, infertility etiology, ovarian reserve tests (AMH, AFC, number of small antral follicles (AFC-a, 2–5 mm), number of large antral follicles (AFC-b, 5–10 mm)) and COS cycle parameters were recorded.

Statistical analysis

Continuous variables are defined as mean ± standard deviation (SD) or median (with 25th-75th percentiles- IQR) depending on the distribution. Categorical variables are defined as percentages. As appropriate, continuous variables were compared between the groups using the independent samples t-test or the Mann-Whitney U test. Categorical variables were compared using the Chi-square test and its derivatives. Binary logistic regression analysis was performed to assess the association of variables with Poor ovarian response. Partial correlation analysis was also performed to study the linear relationship between variables after excluding the effect of independent factors. A two-sided p -value of 0.05 was considered statistically significant.

There were 7458 COS cycles between 2012 and 2022. A total of 1006 patients were enrolled in. There were 404 patients in Group A (uIOR group) and 602 patients in Group B (normo-responder – NOR group). There were 80 patients in Poseidon 1a group and 324 patients in Poseidon 1b group.

The mean age was significantly higher in Group A than in the other group (30.1 + 2.9 vs. 29.4 + 2.9, p  = 0.01). The infertility etiologies were similar between the groups. Although all included patients had a normal ovarian reserve, Group-A had significantly lower AMH and AFC than Group-B [Respectively, with IQR; AMH 1.6 ng/ml (1-2.6) vs. 3.5 ng/ml (2.2–5.4) p  < 0.01, AFC 8 (6–12) vs. 12 (9–17), p  < 0.01 (Figure- 1 )]. When the AFC was further analyzed, the proportion of the small antral follicles (AFC-a, 2–5 mm) was lower in Group-A, while the proportion of larger antral follicles (AFC-b, 5–10 mm) remained similar between the groups. The mean daily gonadotropin dosage was significantly higher in Group-A (302 + 64 IU vs. 250 + 67 IU p  < 0.01).

The collected oocyte numbers (with IQR; 6 (4–8) vs. 15 (12–20) p  < 0.01), metaphase-2 (MII) oocyte numbers (4 (2–6) vs.12 (9–16), p  < 0.01) and two-pronuclei (2PN) embryo numbers [2 (1–4) vs. 7 (5–10), p  < 0.01] were significantly lower in Group-A (Table- 1 ).

The results did not change when the Group-B was further compared with Poseidon subgroups 1a and 1b. It was revealed that Poseidon 1a has significantly lower ovarian reserve and higher age than Poseidon 1b and the control group. However, AFC-b was still similar in each group (Table- 2 ).

Binary logistic regression analysis assessed the association of significant factors on ovarian response. Only AMH and AFC-a showed significance (Table- 3 ). A partial correlation analysis was further performed between AFC-a and impaired ovarian response, excluding the effect of AMH, and the AFC-a showed a positive correlation with ovarian response. The increasing number of small antral follicles is associated with better ovarian response (Table- 4 ).

The present study showed that characteristics of antral follicles at the moment of AFC may help to predict impaired ovarian response (IOR), including both poor and suboptimal profiles, even in women < 35 years of age (POSEIDON groups 1a and 1b). In particular, our results indicated that, even in presence of an apparently normal AFC, the proportion of small antral follicles (AFC-a, 2–5 mm) may be an indicator in predicting IOR in detailed evaluation. The propensity of low ovarian reserve and higher age also emerges as main risk factors for uIOR and POSEIDON 1 profile.

Besides the age and ovarian reserve, numerous factors such as the consumption of high glycemic index foods [ 8 ], the presence of air pollution in the living area [ 14 ], and exposure to substances like benzene raise the risk of uIOR [ 15 ]. Despite studies showed higher concentrations of these substances in the follicle fluids of patients with unexpected poor ovarian response, it is not feasible to accurately assess the dietary habits or exposure to harmful substances and predict uIOR in women with normal ovarian reserve.

Beyond these possible etiologies, the most causatively asserted one is FSH receptor (FSHR) polymorphisms. Since the 90s, many studies have confirmed that FSHR polymorphisms might increase gonadotropin consumption and unexpected poor ovarian response [ 10 ]. Till today, various polymorphisms have been shown to affect ovarian sensitivity negatively. A meta-analysis that involves studies about different FSH & LH receptor genotypes and ovarian sensitivity identified that polymorphisms could untangle this issue in clinical practice [ 16 ]. However, the cost/effectiveness evaluation of screening all the population for FSHR receptor polymorphism to personalize the gonadotropin dose is still matter of debate. Furthermore, a recently published randomized controlled trial showed that the presence of FSHR polymorphism did not negatively affect FORT and FOI scores of the patients and concluded that genotyping the FSHR prior to COS in normo-responder patients should not be routinely recommended [ 17 ].

Another possible etiology that causes uIOR may be incorrect drug administration or insufficient patient education by ART nurses. Despite accurately calculated gonadotropin dosage by ART physicians, patients may inject lower doses of daily gonadotropin or misuse it. Therefore, the main reason for this situation may be inappropriate drug applications that we never anticipated. A multicenter survey study in France reported that almost 20% of the patients misunderstand the drug doses, COS schedule, or application way [ 9 ]. Thus, this possibility should always be kept in mind.

Despite the above-listed possible etiologies for uIOR, predicting both poor and suboptimal profiles in women with normal ovarian reserve is still tricky. The management strategies in the presence of uIOR are mainly based on gonadotropin dose or type adjustment. Firstly, gonadotropin dose increment in the same cycle may be an option when unexpected initial uIOR is observed. De Placido et al. showed that dose increment with recombinant LH might be an effective option for ovarian outcome in patients with an initial inadequate ovarian response to rFSH alone [ 18 ]. On the other hand, dose adjustment in the subsequent cycle may be beneficial in uIOR patients. Drakopoulos et al. reported that an increase of 50 IU of the initial rFSH dose would lead to 1 more oocyte in the subsequent cycle of patients with uIOR [ 2 ]. another COS option in this group of patients is dual stimulation [ 19 ]. As is known, follicular recruitment is an ongoing condition and involves mainly three follicular waves in the one-month cycle [ 20 ]. Therefore, each follicular wave may show fluctuations in antral follicle count, and COS outcome may change cycle by cycle. Eftekhar et al. [ 21 ] studied dual stimulation in patients with uIOR. They reported that the number of collected oocytes was higher in luteal phase stimulation than the follicular phase stimulation in patients with uIOR (Respectively; mean oocyte number; 9.2 + 6.8 vs. 1.9 + 1.1). Cimadomo et al. [ 22 ] also reported that luteal phase stimulation of the same cycle provides more blastocyst in poor prognosis patients.

Although there are various management strategies for patients with a history of uIOR, the main issue is to predict this situation before it happens. The present study aimed to investigate if there is any overlooked predictive factor and results indicated that the proportion of small antral follicles gains importance in predicting the ovarian response. According to our results, a decrease in small antral follicle count causes low FORT and FOI scores. The lower proportion of small antral follicle count (sAFC) is related to ovarian hyposensitivity. Thus, it may be concluded that the proportion of small antral follicles shows functional ovarian reserve. This hypothesis was mainly mentioned in the 90s by different authors. Faddy et al. [ 23 ] showed a biphasic pattern with a steeper decline of follicles after the age of 37.5 years. Scheffer et al. [ 24 ] showed a steeper yearly decline in the number of small antral follicles (2–5 mm) than for larger follicles (6–10 mm). Haadsma et al. [ 25 ] reported that the number of small antral follicles (2–6 mm) is significantly related to age and also, independent of age, to all ovarian reserve tests, suggesting the number of small antral follicles represents the functional ovarian reserve.

Similar to the above-published studies, we previously studied small antral follicle count and its clinical implications in ART. We showed that a high ratio of small-size (2–5 mm) basal antral follicles is a predictive factor for higher ovarian response to ovarian hyperstimulation [ 26 ]. We also demonstrated that between the patients with similar AFC, sAFC proportion determines the AMH discordance independent from age [ 27 ]. As is known, AMH is mainly produced by pre-antral and smaller antral follicles up to 4–6 mm, and an increase in proportion of sAFC provides a higher AMH level [ 28 ]. However, the impact of the increased proportion of small antral follicles (sAFC) on not just the quantity but also the quality of oocytes remains uncertain. Our findings raise the question of whether there is a correlated increase in the rate of immature oocytes associated with the proportion of sAFC. Despite lacking morphological outcomes of the oocytes, our results align with the Maribor criteria, evaluating embryological factors such as the metaphase oocyte rate, fertilization rate, and blastulation rate [ 29 ], demonstrating comparable outcomes.

Our study has some limitations. The retrospective design limits the power of the results. Including only the first COS cycle of each patient, a relatively high number of the groups, and the presence of regression analysis to confirm the significant factors strengthen our findings.

In the realm of clinical practice, the outcomes of our study shed light on the significance of antral follicle characteristics at the time of antral follicle count (AFC) in predicting impaired ovarian response (IOR) among women, even those under the age of 35 (POSEIDON groups 1a and 1b). Notably, our results highlight that, despite an apparently normal AFC, the proportion of small antral follicles (AFC-a, 2–5 mm) may serve as an indicator for predicting IOR during detailed evaluation. The identification of risk factors such as low ovarian reserve and advanced age further underscores the relevance of our findings, offering valuable insights for clinical decision-making.

Looking ahead, future research endeavors should explore integrated predictive models that encompass variables such as the proportion of small antral follicles, age, and AMH levels near the threshold before Controlled Ovarian Stimulation (COS). The combination of these factors has the potential to enhance our ability to forecast ovarian response, aiding in the development of personalized and more effective management strategies. Moreover, additional randomized controlled trials are warranted to validate and refine the insights gleaned from our study, ultimately contributing to the ongoing evolution of tailored approaches for individuals facing impaired ovarian response. The unpredictable nature of uPOR emphasizes the ongoing need for innovative research that can bring us closer to unraveling its complexities and improving outcomes for both patients and clinicians.

figure 1

AMH Histograms of the groups (Figure A refers to patients with < 9 oocytes and Figure B refers to patients with ≥  9 oocytes)

Data availability

All data generated or analyzed during this study are included in this published article and are available from the corresponding author on reasonable request.

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Acknowledgements

We are thankful for tireless efforts of our ART center nurses (Tulay Karatas and Ilkay Cinar) and staff for their precious contributions in all stages of our investigation.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Faculty of Medicine, Dept. of Obstetrics and Gynecology, Bursa Uludag University, Bursa, Turkey

Gürkan Uncu, Kiper Aslan & Isil Kasapoglu

Faculty of Medicine, Dept of Histology and Embryology, Bursa Uludag University, Bursa, Turkey

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Department of Neuroscience, Reproductive Science and Odontostomatology, University of Naples Federico II, Naples, Italy

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Contributions

G.U., K.A.; substantial contributions to the conception, K.A., C.Ç.; data curation, investigation, B.A., I.K.; interpretation of the data, K.A.; writing, C.A., G.U.; review and editing. All authors agreed with the final version of the manuscript.

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Correspondence to Gürkan Uncu .

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Uncu, G., Aslan, K., Cakir, C. et al. Do we overlook predictive factors in Poseidon 1 patients? A retrospective analysis co-evaluating antral follicle counts & diameters. J Ovarian Res 17 , 1 (2024). https://doi.org/10.1186/s13048-023-01323-x

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Received : 11 October 2023

Accepted : 10 December 2023

Published : 02 January 2024

DOI : https://doi.org/10.1186/s13048-023-01323-x

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Journal of Ovarian Research

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