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Research Design – by J. Creswell

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Quantitative vs. Qualitative Research Method Issues Marian Ford Erin Gonzales November 2, 2010.

Reviewing and Critiquing Research

Chapter Four. Writing the Proposal  What does the intended reader/audience need to understand better about the topic?  What does the audience know little.

Introduction to Research

Specifying a Purpose, Research Questions or Hypothesis

Methodology A preview. What is Methodology  Choosing a method of data collection  Structure of the research  Builds on and draws from problem statement.

Problem Identification

Chapter 3 Preparing and Evaluating a Research Plan Gay and Airasian

Reporting and Evaluating Research

Ethics and Methods in Cultural Anthropology

Quantitative and Qualitative Approaches

Chapter One: The Science of Psychology

研究方法論課程報告 報告人：余惟茵 指導老師：任維廉教授

Hypothesis & Research Questions

Quantitative and Qualitative Approaches Dr. William M. Bauer

Chapter One of Your Thesis

Cases, Numbers, Models – by Sprinz & Wolinsky Chapters Three bullet points: Formal models are increasingly useful in developing theory in international.

The Social Science Inquiry Method

Introduction to Educational Research

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QUANTITATIVE RESEARCH METHODS AND DESIGN

AN OVERVIEW OF THE QUANTITIATIVE RESEARCH METHODS. EXPERIMENTAL RESEARCH. SINGE SUBJECT RESEARCH. CORRELATIONAL RESEARCH. CAUSAL COMPARATIVE RESEARCH. DESCRIPTIVE RESEARCH. RESEARCH TOOL AND OBJECTIVES

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Regina Oliver , Joseph Wehby

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Rules and routines are powerful preventative components to classroom organization and management plans because they establish the behavioral context of the classroom by specifying what is expected, what will be reinforced, and what will be retaught if inappropriate behavior occurs (Colvin, Kame’enui, & Sugai, 1993). This prevents problem behavior by giving students specific, appropriate behaviors to engage in. Monitoring student behavior allows the teacher to acknowledge students who are engaging in appropriate behavior and prevent misbehavior from escalating (Colvin et al., 1993). Research on classroom management has typically focused on the identification of individual practices that have some level of evidence to support their adoption within classrooms. These practices are then combined under the assumption that, if individual practices are effective, combining these practices into a package will be equally, if not more, effective. Textbooks are written and policies and guidelines are disseminated to school personnel based on these assumptions. Without research that examines classroom management as an efficient package of effective practices, a significant gap in our current knowledge base still exists. Understanding the components that make up the most effective and efficient classroom management system as well as identifying the effects teachers and administrators can expect from implementing effective classroom management strategies represent some of these gaps. A meta-analysis of classroom management which identifies more and less effective approaches to universal, whole-class, classroom management as a set of practices is needed to provide the field with clear research-based standards. This review examines the effects of teachers’ universal classroom management practices in reducing disruptive, aggressive, and inappropriate behaviors. The specific research questions addressed are: Do teacher’s universal classroom management practices reduce problem behavior in classrooms with students in kindergarten through 12th grade? What components make up the most effective and efficient classroom management programs? Do differences in effectiveness exist between grade levels? Do differences in classroom management components exist between grade levels? Does treatment fidelity affect the outcomes observed? These questions were addressed through a systematic review of the classroom management literature and a meta-analysis of the effects of classroom management on disruptive or aggressive student behavior. Twelve studies of universal classroom management programs were included in the review. The classroom-level mean effect size for the 12 programs was positive and statistically significant (d=.80 with an ICC=.05; d=.71 with an ICC=.10; p<.05). Note that cluster adjustments were required due to differences in reporting measures between classroom level outcomes and individual student level outcomes. The resulting effect sizes index classroom-level differences and cannot be compared to the typical student-level effect sizes commonly reported in the literature. Due to a lack of power to detect heterogeneity and lack of information reported in the studies reviewed, only the first research question could be addressed. Teacher’s classroom management practices have a significant, positive effect on decreasing problem behavior in the classroom. Students in the treatment classrooms in all 12 studies located for the review showed less disruptive, inappropriate, and aggressive behavior in the classroom compared to untreated students in the control classrooms. The overall mean classroom effect size of either .71 or .80 indicates a positive effect that significantly impacts the classroom environment. To put our classroom-level mean effect sizes into a comparable format with the more typical effect sizes, we back-transformed our mean effect sizes using the original adjustment formulas (Hedges, 2007). Thus, the classroom-level mean effect sizes of .80 and .71 are roughly comparable to student level effect sizes of .18 and .22 for ICC=.05 and ICC=.10, respectively. Teachers who use effective classroom management can expect to experience improvements in student behavior and improvements that establish the context for effective instructional practices to occur.

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Research suggests a compelling correlation between teacher behavior and effective learning environments. Focusing on the evidence-based teaching skill of offering behavior-specific praise (BSP), the researchers worked with three elementary-level general educators in a tiered model of training generally known as response to intervention (RtI). Although RtI commonly provides targeted instructional support to students, this study used the RtI framework to provide professional development instruction to teachers. The researchers also tracked the behavior of three students identified by the teachers as having behavioral difficulties, who became the focus of each teacher’s BSP. Results showed increases in rates of BSP following the Tier 2 and Tier 3 interventions (video self-monitoring and peer coaching), but not following the Tier 1 intervention (school-wide in-service training). Averagesfor all three students’ on-task behavior increased with increased teacher BSP.

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Quantitative Research Designs - PowerPoint PPT Presentation

Quantitative Research Designs

Quantitative research designs v. darleen opfer conditions for establishing cause-effect relationships: covariation temporal precedence no plausible alternative ... – powerpoint ppt presentation.

• V. Darleen Opfer
• Covariation
• Temporal Precedence
• No Plausible Alternative Explanations
• Instrumentation
• Selection by maturation interaction
• Ambiguity about causal direction
• Diffusion of treatments
• Compensatory equalization of treatments
• Compensatory rivalry
• By argument
• By measurement or observation
• By analysis
• By preventive action
• Program(s) or Treatment (s) X
• Observation(s) or Measure(s) O
• Groups or Individuals
• R Randomly assigned groups
• Expanding across time
• O X O O X O
• Expanding across programs
• Expanding across observations
• O1O2 X O1O2
• Expanding across groups
• ______________________________
• Depict the hypothesized causal relationship
• Identify the possible alternative explanation threats
• Over-expand the basic design by expanding across time, program observations, and groups accounting for as many alternative explanations as possible
• Scale back the design to a manageable plan by considering the effect of eliminating each design component
• Theory-Grounded
• Situational
• A principal/head teacher wants to know whether the provision of professional development will improve the instructional practices of teachers.
• Design a study for answering her question.
• Address the following questions
• What are your variables? (What are will you be measuring?)
• What are potential validity/alternative explanation issues with this study?
• What are the validity issues accounted for in your approach? And how would you minimize those not accounted for in the design?

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Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Quantitative Research Designs

Jan 06, 2020

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9. Quantitative Research Designs. Learning Objectives. Identify Criteria For Exploratory, Descriptive, And Explanatory Studies Define Experimental Research Differentiate Between Internal And External Validity In Experimental Designs Identify Six Threats To Internal Validity

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Presentation Transcript

9 Quantitative Research Designs

Learning Objectives • Identify Criteria For Exploratory, Descriptive, And Explanatory Studies • Define Experimental Research • Differentiate Between Internal And External Validity In Experimental Designs • Identify Six Threats To Internal Validity • Identify Three Threats To External Validity

Learning Objectives Distinguish Among True Experimental, Quasi-Experimental, And Pre-Experimental Designs Describe Three True Experimental Designs Describe Two Quasi-experimental Designs Describe Two Pre-experimental Designs

Learning Objectives Discuss Four Types Of Nonexperimental Research Designs Recognize Two Types Of Settings In Which Research Is Conducted Identify Factors That Influence The Choice Of Research Designs Critique The Design Section Of Quantitative Studies

Learning Objective OneIdentify Criteria For Exploratory, Descriptive, And Explanatory Studies

Exploratory Studies • Little known about phenomenon • Flexible data collection approach • Qualitative and quantitative • Hypotheses not appropriate

Descriptive Studies • Phenomena described • Relationship between variables examined • More information about variable(s) • Test hypotheses

Explanatory Studies • Explanations for relationships among phenomena • Rigorous • Experimental research • Control over research conditions • Manipulate one or more variables

Learning Objective TwoDefine Experimental Research

Experimental Research • Cause + effect • Manipulate and control independent variable • Measure dependent variable

Problems With Experimental Research • Casual relationships difficult to establish • Avoid using word prove • Controls difficult to apply to human beings

Learning Objective ThreeDifferentiate Between Internal And External Validity In Experimental Designs

Internal Validity • Degree to which changes in effect can be attributed to cause • Threats • Other factors that influence dependent variable • Constitute rival explanations or competing hypotheses

External Validity • Degree to which results can be generalized • Questions to ask • With what degree of confidence can findings be transferred to the entire population? • Will these findings hold true with other groups and in other times and places?

Relationship Between Internal and External Validity • As control for internal increases, external decreases • As concern for external increases, internal may be affected • Need to find balance

Learning Objective FourIdentify Six Threats To Internal Validity

Six Threats to Internal Validity • Selection bias • History • Maturation • Testing • Instrumentation change • Mortality

Selection Bias • Results due to subject differences • Not due to independent variable manipulation • Means to control • Random group assignment

History • Event other than the experimental treatment occurs during the course of study. • Event influences dependent variable. • Means to control • Simultaneous control and comparison groups • Random assignment of subjects to groups

Maturation • Changes occur within subjects during study. • Changes influence the study results. • Means to control • Simultaneous control and comparison groups

Testing • Influence of pretest or baseline data knowledge on posttest score

Instrumentation Change • Difference between pretest and posttest measurement • Caused by change in accuracy rather than experimental treatment • Means to control • Judge training sessions • Trial instrument runs to check for changes • Continue to check instrument accuracy

Mortality • Subject does not complete study. • Attrition rate different between groups • Means to control • No research design to control • Establish strong researcher-participant relationship

Learning Objective FiveIdentify Three Threats To External Validity

Major Threats to External Validity • Hawthorne effect • Experimenter effect • Reactive effects of the pretest

Hawthorne Effect • Participants’ responses influenced by knowing they are being observed • Means to control • Double-blind experiment

Experimenter Effect • Experimental research • Researcher characteristics or behaviors influence subject behaviors. • Examples of influential characteristics • Facial expression • Clothing • Age • Gender • Body build

Rosenthal Effect • Nonexperimental research • Interviewer characteristics or behaviors influence respondent’s answers.

Reactive Effects of the Pretest • Subjects sensitized to experimental treatment because of pretest • Examples of pretests • Paper-and-pencil test • Knowledge of baseline data

Difference Between Internal and External Pretest Threats • Internal threat: pretest or baseline data knowledge cause of posttest results • External threat: pretest or baseline data knowledge catalyst (indirect cause)

Learning Objective SixDistinguish Among True Experimental, Quasi-Experimental, AndPre-Experimental Designs

Experimental Research Designs • True experimental • Quasi-experimental • Pre-experimental

True Experimental Design • Great deal of control • Internal validity threats minimized • Causality inferred with confidence

True Experimental Design Criteria • Researcher manipulates the experimental variable(s). • One experimental group and one comparison group • Subjects randomly assigned to groups

Quasi-Experimental Design • No comparison group • Subjects not randomly assigned to groups

Advantages and Disadvantages of Quasi-Experimental Design • Advantages • Real world more closely approximated • Disadvantages • Not as much control as true experimental design

Pre-Experimental Design • Considered weak • Researcher has little control.

Learning Objective SevenDescribe Three True Experimental Designs

Experimental Designs • Pretest-posttest control group design • Posttest-only design • Solomon four-group design

The Pretest-PosttestControl Group Design • Most frequently used experimental design • Criteria • Subjects randomly assigned to groups • Pretest given to both groups • Experimental group receives experimental treatment. • Comparison group receives routine treatment or no treatment. • Posttest given to both groups

The Pretest-PosttestControl Group Design (cont’d) • Advantages • Controls for all internal validity threats • Controls for initial differences by adjusting posttest scores statistically • Disadvantages • External threat of reactive effects of the pretest • Can only be generalized to situations in which pretest is administered

The Posttest-OnlyControl Group Design • Subjects randomly assigned to groups • Experimental group receives the experimental treatment. • Comparison group receives routine treatment or no treatment. • Posttest given to both groups

Advantages of Posttest-Only Control Group Design • Easier to carry out • Eliminates reactive effects of the pretest on the posttest

The Solomon Four-Group Design • All subjects are randomly assigned to one of four groups. • Two groups—experimental group 1 and comparison group 1— pretested • Two groups—experimental group 1 and experimental group 2—receive experimental treatment. • Two groups—comparison group 1 and comparison group 2—receive routine treatment or no treatment.

Solomon Four-Group Design • Posttest given to all four groups • Advantages • Minimizes threats to both internal and external validity • Differences between groups can be associated with the experimental treatment. • Disadvantages • Requires a large sample • Statistical analysis is complicated.

Learning Objective EightDescribe Two Quasi-Experimental Designs

Quasi-Experimental Designs • Nonequivalent control group design • Time-series design

The NonequivalentControl Group Design • Similar to pretest-posttest control group design • No random assignment of subjects to groups

Internal Validity in Nonequivalent Control Group Design • Threats to internal validity controlled • History • Testing • Maturation • Instrumentation change • Threats to internal validity that remain • Selection bias

Time-Series Design • Periodic observations or measurements of subjects • Experimental treatment administered between two of the observations

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