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Mixed methods research

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Introduction to Psychology

SI0030 – Social Research Methods Week 10 Luke Sloan Mixed Methods Week 10 Luke Sloan Mixed Methods.

Issues in a research team using different methods: Changing hats? Julia Brannen Thomas Coram Research Unit Institute of Education, University of London.

About this Template In the past decades, mixed methods research has been increasingly applied to the dominant research fields of social, behavioral or.

Chapter 17 Mixed Method Designs

Qualitative Data Analysis and Interpretation

Chapter 17 Mixed Methods Designs

 It’s an approach to research that examines a concept or phenomenon from the perspective of the individual who is experiencing it  The research purpose.

Quantitative and Qualitative Approaches

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Quantitative and Qualitative Approaches Dr. William M. Bauer

Research Design Mixed Methods

RESEARCH DESIGN.

Chapter 19: Mixed Methods Research

+ Using Mixed Methods Research Designs for Research in Teaching and Learning Dr. Elizabeth G. Creamer, Virginia Tech Dr. Beth Mac Donald, Utah State University.

Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 18 Mixed Methods and Other Special Types of Research.

RESEARCH IN MATH EDUCATION-3

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• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

Published on August 13, 2021 by Tegan George . Revised on June 22, 2023.

Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question . Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.

Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.

• To what extent does the frequency of traffic accidents ( quantitative ) reflect cyclist perceptions of road safety ( qualitative ) in Amsterdam?
• How do student perceptions of their school environment ( qualitative ) relate to differences in test scores ( quantitative ) ?
• How do interviews about job satisfaction at Company X ( qualitative ) help explain year-over-year sales performance and other KPIs ( quantitative ) ?
• How can voter and non-voter beliefs about democracy ( qualitative ) help explain election turnout patterns ( quantitative ) in Town X?
• How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative) ?

Table of contents

When to use mixed methods research, mixed methods research designs, advantages of mixed methods research, disadvantages of mixed methods research, other interesting articles, frequently asked questions.

Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:

• Generalizability : Qualitative research usually has a smaller sample size , and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
• Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
• Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation .

As you formulate your research question , try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.

But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.

Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions.

Mixed methods can be very challenging to put into practice, and comes with the same risk of research biases as standalone studies, so it’s a less common choice than standalone qualitative or qualitative research.

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There are different types of mixed methods research designs . The differences between them relate to the aim of the research, the timing of the data collection , and the importance given to each data type.

As you design your mixed methods study, also keep in mind:

• Your research approach ( inductive vs deductive )
• Your research questions
• What kind of data is already available for you to use
• What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.

• On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
• On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.

This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.

Explanatory sequential

In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.

You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.

Exploratory sequential

In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.

You can use this design to first explore initial questions and develop hypotheses . Then you can use the quantitative data to test or confirm your qualitative findings.

“Best of both worlds” analysis

Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable , externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.

For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.

Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.

Method flexibility

Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.

Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.

Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.

Differing or conflicting results

If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables , it can be unclear how to proceed.

Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results, putting your data at risk for bias in the interpretation stage.

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.

• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

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

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

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

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

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

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

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Chapter 8: Research designs using mixed methods

2014, Doing Research in the Real World, 3rd edn

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Mixed Research Methods. Debra D. Roberts Department of Psychology HOWARD UNIVERSITY. SCIENTIFIC METHOD. Process involving a systematic approach to assessing observed (behavior) through the use of empirical evidence Steps Identify a Problem

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Mixed Research Methods Debra D. Roberts Department of Psychology HOWARD UNIVERSITY

SCIENTIFIC METHOD • Process involving a systematic approach to assessing observed (behavior) through the use of empiricalevidence • Steps • Identify a Problem • Develop a Hypothesis or Research Question • Gather Empirical Evidence • Analyze Data • Disseminate Findings

Asian Americans are frequently deployed as racial mascots by pundits who fixate on their extraordinary levels of educational attainment. They comprise only 5.5% of the U.S. population, yet about one fifth of the entering classes in Ivy League universities like Harvard, Yale, and Princeton. Pundits have attributed these educational outcomes to cultural factors, underpinned by values or traits that are innately Asian. However, this cultural explanation fails to consider the pivotal role of U.S. immigration law which has ushered in a new stream of highly educated, highly skilled Asian immigrants. Hyper-selectivity (as opposed to hypo-selectivity) of contemporary immigration significantly influences the educational trajectories and outcomes in the members of the 1.5 and second generation beyond individual family or parental socioeconomic characteristics, leading to group-based advantages (or disadvantages) that are consequential. Analysis of qualitative data shows that the children of hyper-selected immigrant groups begin their quest to get ahead from more favorable starting points, are guided by a more constricting success frame, and have greater access to ethnic capital than those of other immigrant groups. In turn, hyper-selectivity gives rise to stereotype promise —the boost in performance that comes with being favorably perceived and treated as smart, high-achieving, hardworking, and deserving students—that benefits members of the group so stereotyped. Our analysis also suggests that, while the so-called positive stereotype enhances the academic performance of Asian American students, the same stereotype reproduces new stereotypes that hinder them as they pursue leadership positions in the workplace. We suggest that Asian American professionals face a bamboo ceiling—an invisible barrier that impedes their upward mobility much like the glass ceiling does for women. 

COLLECT DATA • Identify the BEST research design to fit the question • QUALITATIVE vs. QUANTITATIVE • Qualitative Research aims to complete a detailed description • Quantitative Research aims to classify features, count them, and construct statistical models in an attempt to explain what is observed

QUALITATIVE Researcher may only know roughly in advance what he/she is looking for Researcher is the data gathering instrument Data are in the form of words, pictures or objects Dataset is more “rich”, time consuming, and less able to be generalized Researcher tends to become subjectively immersed in subject matter QUANTITATIVE Researcher knows clearly in advance what he/she is looking for Researcher uses tools (questionnaires) to collect numerical data Data are in the form of numbers and statistics Dataset is more efficient, but may miss contextual detail Researcher tends to remain objectively separated from the subject matter THE DEBATE

EXPERIMENTAL METHODS • Involves the manipulation (or observation) of variables • Variable: Any event, situation, or behavior that has at least two values • Variables can be classified in a number of ways

CATEGORIES OF VARIABLES • Situational Variables • Describe characteristics of a situation or environment • Response Variables • Responses or behaviors of individuals • Participant Variables • Characteristics of the individual • Mediating Variables • Processes that mediate the effects of a situational variable or particular response

TYPES OF VARIABLES • Independent (IV) • Manipulated by the researcher • Considered to be the “cause” • Dependent (DV) • Observed change as a result of manipulated IV • Confounding (third variable factor) • Unintentionally contributed to the observed change in the DV

OPERATIONAL DEFINITION • REMEMBER: Research is about making observations of human behavior, attitudes, etc. in a systematic manner • VARIABLES are simply abstract concepts that must be translated into concrete forms of observation • OPERATIONAL DEFINITION: Describing the variable in terms of the operations or techniques the researcher uses to measure or manipulate it

QUANTIFYING CONSTRUCTS • We often utilize scales of measurement • Nominal • Values of the scale have no 'numeric' meaning • Ordinal • Scale assignment is by the property of "greater than," "equal to," or "less than." • Interval • Intervals between values are equal • Ratio • There is an absolute zero point for the scale

A FEW EXAMPLES • Researcher A wants to study the effects of exposure to different types of music on anxiety among college students • IV = Type of music you listen to • Operationalized as: Choice between genres of music (classical, reggae, hip hop, jazz, etc.) • DV = Anxiety • Operationalized as: Standardized measure of anxiety

EXAMPLE 2 • Researcher B notices a clear difference in prevalence of asthma symptoms between those children living in impoverished neighborhoods and those living in more affluent neighborhoods. She sets up a study as follows: • IV = Neighborhood • Group 1: Impoverished as defined by census data or median income • Group 2: Affluent as defined by census data or median income • DV = Prevalence of Asthma Symptoms • Use frequency checklist to be completed by parents

EXAMPLE 3 • After Hurricane Katrina, researcher C noticed a marked increase in the number of parents reporting that their child was experiencing night terrors. He wants to test the hypothesis that trauma causes night terrors: • IV = Level of Trauma • Group 1: Witnessed the hurricane but family not displaced • Group 2: Witnessed the hurricane and family displaced • Group 3: Did not witness the hurricane • DV = Night terrors • Experience night terrors post-hurricane (yes OR no)

NON-EXPERIMENTAL VS. EXPERIMENTAL • There are advantages and disadvantages to both methods • Experimental methods offer more “researcher” control • Experimental methods allow for the inference of “causation”

THREATS TO VALIDITY • Subject Loss (selective) • History • Maturation • Demand Characteristics • Experimenter Effects • Testing

“TRUE” EXPERIMENTAL DESIGN • Involves the direct manipulation of the variable(s) • Subjects must be assigned to groups • Randomization ensures that extraneous variables are taken into account Independent Groups Design • Experimental Group • Receives the treatment • Control Group • Receives no treatment (or placebo)

QUASI-EXPERIMENTAL DESIGN • Provides an important alternative when true experiments are not possible (usually occur in natural settings) • Lacks the degree of control found in true experiments • Consider threats to internal validity

PRETEST-POSTTEST • Step 1: Observe (O1) outcome/dependent variable among group members (baseline measure) • Step 2: Administer treatment (X) • Step 3: Observe outcome (O2) variable post treatment administration O1 X O2

NONEQUIVALENT CONTROL GROUP DESIGN • Better able to make causal claims by adding a comparison group to a simple pretest-posttest design • Observations are made at time 1 and time 2 for both groups O1 X O2 ------------------ O1 O2

COMPLEX DESIGNS • Studying the effects of two or more IVs in one experiment • Main effects vs. Interaction Effects • Describe design: • Identify number of IVs and number of levels for each IV • Class Examples…

INTERACTIVE EXAMPLES

DATA COLLECTION / ANALYSES • Decide what your units of analyses are • Operationalize your variables • Decide what your data collection method is • Survey, Interview (focus group), Instruments • Be mindful of your analytical procedures when collecting data • Measurement determines types of analyses

ANALYSES • Descriptive Statistics: Used to answer the question “What happened in the experiment?” rather than “Why it happened.” • Inferential Statistics: We want to know whether the IV has a reliable effect on the DV • Usually Alpha (α) set at .05

QUALITATIVE APPROACHES • PHENOMENOLOGICAL • ETHNOGRAPHIC • GROUNDED THEORY • ENDOGENOUS

PHENOMENOLOGY • PURPOSE: To help researchers understand participants’ point of view • PROCESS : Select topic that is personally meaningful (emotionally and intellectually engaging) • DATA COLLECTION: Knowledgeable and informative dialogue or conversation with 5-10 participants • DATA ANALYSIS: Open, tentative, intuitive and meaningful • COMMUNICATING FINDINGS: Biographical sketches

GROUNDED THEORY • PURPOSE: To increase understanding • PROCESS : Spend time interacting with participants • DATA COLLECTION: Field work • DATA ANALYSIS: Interpretational, structural, and reflective • COMMUNICATING FINDINGS: Descriptive and narrative

ETHNOGRAPHY • PURPOSE: To describe accurately the lived experiences of people – the relationship between culture and behavior. • PROCESS : Interactive • DATA COLLECTION: Participant observation, interviews and artifacts • DATA ANALYSIS: Comparing/contrasting • COMMUNICATING FINDINGS: Holistic description of people being observed

ENDOGENOUS • PURPOSE: To yield insider perspective through involvement of subject as researcher • PROCESS : Subjects of inquiry become the researchers – control of research plan is relinquished • DATA COLLECTION: Conducted by “insiders” of the culture • DATA ANALYSIS: Interpretational, open, and reflective • COMMUNICATING FINDINGS: Descriptive and narrative (from the subjects’ perspectives)

Mixed Methods • The research approach in which both quantitative and qualitative methods are used • Compatibility thesis • Position that quantitative and qualitative research methods and philosophies can be combined • Pragmatism • Philosophy focusing on what works as the criterion of what should be viewed as tentatively true and useful in research and practice • Questions to be answered when using a mixed design • Should you primarily use one methodology or treat them equally? • Should phases of study be conducted concurrently or sequentially? Christensen et al., 2014

KEY CONCEPTS • Fixed vs. Emergent Designs • Fixed: Use of Mixed Methods is Predetermined by Investigator • Emergent: Use of Mixed Methods arise as a result of issues that arise after research is underway

CORE DESIGNS • Convergent (Concurrent/Parallel) • Intent is to compare or combine results to obtain a more complete understanding of a problem • Explanatory Sequential • Qualitative results help to inform or explain quantitative results • Exploratory Sequential • Quantitative study designed based on qualitative results • Fixed: Use of Mixed Methods is Predetermined by Investigator • Emergent: Use of Mixed Methods arise as a result of issues that arise after research is underway

Three Core Mixed Methods Designs Creswell, 2018

Strengths Christensen et al., 2014

VALIDITY • Inside – outside validity • Present when the researcher provides both the insider and objective outsider perspectives • Weakness minimization validity • Present when the researcher compensates for the weakness of one approach through the use of an additional approach • Sequential validity • Making sure that the ordering of quantitative and qualitative components in a sequential design does not bias the results Christensen et al., 2014

VALIDITY • Sample integration validity • Researchers must not treat the quantitative and qualitative samples as equal, but, instead, draw appropriate conclusions from each sample • Multiple validities • Making sure your mixed methods study meets appropriate quantitative, qualitative, and mixed methods validity types Christensen et al., 2014

DESIGN SCHEME • Based on two dimensions: • Time Order • One of the two dimensions used in MM design matrix; its levels are concurrent and sequential • Paradigm Emphasis • One of the two dimensions used in MM design matrix; its levels are equal status and dominant status Christensen et al., 2014

TIME ORDER & PARADIGM EMPHASIS Morse, 1991

TYPES OF QUESTIONS BY DESIGN Depoy & Gitlin, 1998

Mixed Methods Approach:Example 1 QUANT Qual Evaluating Hope and Hardiness in Caribbean Immigrant Women Operationalize Variables: Scales Random selection of subgroup from top 10% of hardiness scores Analyze interviews by identifying common themes (e.g. frequency)

Mixed Methods Approach:Example 2 QUAL Quant Developing a Measure Begin with Focus Group Pull out Themes Develop Questions Factor Analysis Pilot Testing

Mixed Methods Approach:Example 3 QUAL + QUANT Small sample of middle school students in unique setting Ideal context to “hear” from members of marginalized group Culturally informed, strengths-based approach

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