Mixed Methods Research

Mixed methods research combines quantitative and qualitative methods in a single study or a linked series of studies. The two strands are integrated on purpose, so the numbers and the narratives together answer questions that neither could answer alone.

What are mixed methods?

Mixed methods research integrates both quantitative and qualitative research methods within a single study or across multiple related studies.

• Quantitative Methods: Used to identify trends and relationships between variables, uses methods such as experiments, surveys, biological measures, and structured observations to gather numerical data.
• Qualitative Methods: Valuable for understanding participant experiences and perspectives, employs methods like interviews, focus groups, analysis of open-ended text, and observational field notes to gather non-numerical data.

Mixed methods research is particularly useful when a question needs a multifaceted approach. It can explore trends in data and the nuances of individual experiences at the same time.

Integration is the key concept in mixed methods research. It means combining quantitative and qualitative research on purpose, so the two strands become interdependent and work toward a common research goal.

For example, a researcher might examine changes in school funding (quantitative) alongside teacher and student narratives about educational quality in a school district (qualitative). Together, the two give a fuller picture of how funding relates to the actual experiences of people inside the school system.

What Makes Mixed Methods Research Distinct?

Three features set mixed methods research apart from running two separate studies:

• Rigorous Methods: It is not sufficient to merely include both quantitative and qualitative components within a study; both the quantitative and qualitative strands of the research should follow rigorous methods independently. One way to ensure this rigor is to match quantitative and qualitative data sources to guarantee parallel concepts are investigated.
• Integration: The two approaches are combined on purpose, creating interdependence and synergy. Integration can happen at the design, methods, and interpretation and reporting levels.
• Rationale: Researchers must clearly justify their reason for utilizing a mixed methods design, demonstrating that a mixed method approach is either necessary or will yield superior results in comparison to using a single methodology.

Examples

Examples of Mixed Methods Research in Real Life

Mixed methods research is a powerful tool that can be used to answer complex research questions in a way that neither quantitative nor qualitative research can do alone:

• School mental health program: Interviews with students and teachers reveal barriers and facilitators to implementation. They help explain variations in outcomes such as changes in depression or anxiety symptoms.
• Patient satisfaction after surgery: Patient interviews or focus groups reveal why satisfaction varies. Researchers then connect these themes with complications, recovery time, or readmission rates to look for patterns.
• Teacher training: Student test scores before and after training are combined with teacher interviews and classroom observations. Together they show whether gains reflect changes in teaching practice.

When to use mixed methods research

Researchers should clearly articulate their reasons for using a mixed methods research design. This rationale helps reviewers and other researchers understand why this design is the most appropriate for addressing the research questions.

1. Triangulation: When researchers want to double-check their findings, they can use mixed methods. This involves comparing results from quantitative and qualitative strands to confirm findings and enhance the validity of the study.
2. Complementarity: When researchers need to explain confusing results, they can use mixed methods to get a clearer picture. This aims to elaborate on or clarify the findings of one strand with the results of the other strand. This approach can be particularly useful when quantitative findings are statistically significant but lack practical meaning or when qualitative findings need further clarification.
3. Development: When researchers need to design a good survey or test, they can use mixed methods. This rationale involves using the results from one method to help develop or inform the other method. This can include using qualitative findings to develop and validate an instrument for the quantitative strand, or using quantitative findings to identify specific participants or groups for the qualitative strand.
4. Initiation: This is when researchers want to explore differences in findings from different methods. By comparing different perspectives, they can develop new interpretations of what they’re studying. It leverages the strengths of each approach to clarify, contextualize, and enrich the overall findings, rather than focusing on resolving contradictions.
5. Expansion: When researchers want to learn more about something, they can use mixed methods. This rationale seeks to expand the breadth and range of a study by using mixed methods to investigate different components of a research question or to study different research questions within the same study.

Types of Mixed Methods Research Designs

Mixed methods research designs provide researchers with a structured approach to combining quantitative and qualitative data. Creswell and Plano Clark (2018) identify three core mixed methods designs, to which embedded and multiphase variants are commonly added:

Convergent Parallel Design

Convergent parallel design involves collecting quantitative and qualitative data simultaneously and analyzing the two datasets separately. The researcher then merges the results, bringing them together for direct comparison, to interpret what they show.

For example, researchers have matched semi-structured interviews with survey data to understand domestic violence and abuse among gay and bisexual men (Bacchus et al., 2018).

How Convergent Parallel Design Works

• The process includes collecting data for both strands concurrently but separate from each other, analyzing each independent strand, and merging them.
• The key feature is that both types of data are given equal priority and are collected within a short time interval or simultaneously.
• Researchers then compare and contrast the findings to develop a more comprehensive understanding of the research problem.

Worked Example: Bacchus et al. (2018)

Both strands came from the same clinical population and were analyzed in the terms of their own traditions, which is what makes the comparison meaningful. Treating the tension between a favorable statistic and a more qualified narrative as a finding in itself illustrates expansion.

All participants were in contact with one UK sexual health service, so the findings may not generalize to gay and bisexual men outside such services.

Explanatory Sequential Design

An explanatory sequential design is used in research when you want to use one type of data to help explain the findings of another type.

For example, this design can be used to learn more about why students stayed enrolled in online education programs. The researchers could first examine survey data and then follow up with interviews to understand the factors involved in more depth.

How Explanatory Sequential Design Works

• You start by collecting and analyzing quantitative data. This usually means collecting numbers, like from surveys, and analyzing them to see if there are any patterns or relationships.
• Next, use the quantitative results that are significant, surprising, or in need of explanation to guide your qualitative data collection. Qualitative data usually involves words and stories, like what you would get from interviews.
• You analyze the qualitative data to see if they can help you understand the patterns you saw in the quantitative data. For example, you might have found a surprising trend in a survey, and now you can use interviews to better understand why that trend exists.
• Finally, you combine what you learned from both types of data to get a more complete understanding of what you’re researching

Exploratory Sequential Design

An exploratory sequential design is a type of research that involves two phases of data collection and analysis, with the qualitative phase coming first.

The exploratory sequential design works best when researchers have a clear rationale for mixing methods. The research questions should also lend themselves to both qualitative and quantitative exploration.

How Exploratory Sequential Design Works

• Phase 1: Qualitative Exploration: Researchers begin by collecting and analyzing qualitative data. This typically involves gathering in-depth information from a smaller group of participants through methods like interviews, focus groups, or observations. The goal of this phase is to gain a rich understanding of the experiences, perspectives, and meanings associated with the research topic.
• Phase 2: Quantitative Expansion: The findings from the qualitative phase are then used to inform the design of the quantitative phase. This might involve developing a survey instrument based on the themes that emerged from the qualitative data, identifying specific variables to measure, or creating an intervention to test.
• Integration: In the final step, researchers integrate the findings from both phases to develop a more comprehensive understanding of the research topic. This might involve comparing the quantitative results to the qualitative findings, using the qualitative data to explain unexpected quantitative results, or developing a theory based on the combined insights from both phases.

Example: Brain Donation Decisions

Researchers were interested in understanding the factors that influence brain donation decisions among older minorities.

They started by conducting interviews with individuals from this population to explore their thoughts, beliefs, and experiences related to brain donation.

The themes and insights from these interviews were then used to develop a survey instrument to measure the factors identified as potentially influencing donation decisions.

Worked Example: Francois-Walcott et al. (2024)

The study shows the design doing its job. A smaller qualitative phase surfaced a pattern that existing theory had not anticipated, and a larger quantitative phase tested whether it held and how large it was.

The design also has a built-in limit. The second phase can only test what the first phase manages to surface.

Embedded (or nested) Design

Embedded designs nest one approach inside the other to yield greater insight. A typical case is supplemental qualitative data in a larger experimental trial.

The two kinds of data collection and analysis are linked at multiple points, which makes this design particularly useful in intervention research. They can be variants of convergent or sequential designs.

Qualitative data can play a role at three points. Before the intervention, they can inform recruitment strategies or help develop the intervention. During the experiment, they can examine the process participants are experiencing. After it, they can follow up and help explain the quantitative outcomes.

Connecting, Building, and Merging Data

• Connecting links data through sampling. For example, in a study with surveys and interviews, participants for the interviews might be chosen from those who completed the survey.
• Building uses one type of data to inform the other. An example of this is when researchers analyze baseline survey data and use those findings to design interview questions.
• Merging involves combining qualitative and quantitative data to directly compare results.

Qualitative Data Across a Trial

• Pretrial: Qualitative data, or a mix of qualitative and quantitative data, can help clarify outcome measures, understand factors that might lead to bias, or develop tools for use during the trial.
• During the Trial: Qualitative data helps understand contextual factors that might influence results, providing detailed information about the subjects’ experiences.
• Post-Trial: Researchers use qualitative data to explain outliers, debrief subjects or researchers, or create hypotheses for implementation.

Example: Yoga Alongside CBT

A clinical example shows the post-trial use. Capon, O’Shea, Evans, and McIver (2021) reported the qualitative arm of a larger mixed methods trial. The trial tested therapeutic yoga as an adjunct to group cognitive behavior therapy (CBT) for adults with anxiety and depression.

The researchers interviewed 27 adults who had chosen the yoga program, straight after the eight-week program and again three months later. They analyzed the interviews thematically.

Participants experienced yoga as an embodied way of practicing CBT skills, such as breathing techniques and behavioral activation. Several kept using it afterwards as a self-management and relapse-prevention tool.

The qualitative arm did not test whether the adjunct improved outcomes. That was the job of the trial’s quantitative strand. Instead, it supplied the explanatory data on mechanism and acceptability that outcome scores alone cannot.

Multiphase Design

A multiphase design combines elements of sequential and convergent designs across more than two phases (Creswell and Plano Clark, 2018). It unfolds over an extended period.

It resembles a program of related studies. Rather than mixing data once, it cycles through quantitative and qualitative methods repeatedly as a program or intervention is designed, implemented, and evaluated.

A team might develop a training program, test it, then refine it. Each phase uses whatever methods its own question needs.

Because it is effectively several linked studies working toward one long-term goal, the design suits program evaluation and sustained research programs well. It is also the most time-consuming and hardest to coordinate of the standard designs.

Each phase can need its own ethical approval. Institutional review may therefore be required more than once.

Integration

Integration is the intentional combination of quantitative and qualitative research, resulting in a synergistic and interdependent relationship between the two approaches. It is a multifaceted concept that manifests across various stages of the research process, from design to reporting.

Integration Trilogy: Design, Methods, and Interpretation & Reporting

Integration in mixed methods research operates at three distinct but interconnected levels, often referred to as the integration trilogy (Fetters et al., 2013):

1. Integration at the Design Level: This involves selecting a mixed methods design that outlines the sequence, priority, and purpose of integrating the quantitative and qualitative strands. Common designs include exploratory sequential, explanatory sequential, and convergent designs.
2. Integration at the Methods Level: Integration strategies are the methods employed to combine quantitative and qualitative research elements. These strategies are not mutually exclusive and can be used in various combinations to achieve a holistic understanding of the research problem.
   • Merging: This involves analyzing data from both strands and assessing whether the findings converge, diverge, or expand upon one another. This can involve comparing themes with statistical data, exploring the quantitative profile of qualitative themes, or transforming qualitative data into quantitative data for statistical analysis. Joint displays, such as tables or matrices, can visually represent merged data.
   • Connecting: This involves using one type of data to inform the sampling frame of the other. For instance, quantitative data can be used to identify a subset of participants for qualitative interviews. This strategy is particularly useful in sequential designs, where one strand precedes the other.
   • Building: This approach uses one database (qualitative or quantitative) to inform the data collection approach of the other. This could involve developing a quantitative instrument based on themes identified through qualitative research or refining a qualitative interview guide based on quantitative findings.
3. Integration at the Interpretation and Reporting Level: This involves combining and presenting the findings in a way that highlights the synergistic insights gained from integrating the two strands. This can be achieved through narrative techniques like weaving and contiguous approaches or through data transformation and joint displays.
   • Integrating through narrative involves using a single report, or a series of reports, to describe the quantitative and qualitative results. The Survival After Acute Myocardial Infarction (SAMI) study provides an example of a narrative integration.
   • Integrating through data transformation involves converting one type of data (qualitative or quantitative) into the other type. For example, qualitative data may be converted into numerical counts, which are then integrated with other numerical data for analysis.
   • Integrating through joint displays involves using visual elements, such as tables, matrices, or figures, to present the integrated data. An example of a study using joint displays is a mixed-methods evaluation that explored ethical aspects of adaptive clinical trial designs.

Narrative Integration

Integrating through narrative in mixed methods research involves describing qualitative and quantitative findings within a single report or a series of reports. The manner in which these findings are presented can take on three distinct approaches: weaving, contiguous, or staged.

• Weaving presents qualitative and quantitative findings together, interlacing them theme-by-theme or concept-by-concept. For example, Classen et al. intertwined results from a national crash dataset and stakeholder perspectives to understand the causative factors of vehicle crashes among the elderly and to formulate guidelines for public health interventions.
• Contiguous integration entails presenting findings within a single report, but the qualitative and quantitative findings are segmented into distinct sections. For instance, presenting survey findings in the initial part of the results section and qualitative findings about contextual factors in a subsequent part of the report.
• The staged approach is frequently employed in multistage mixed methods studies, where the results from each stage are reported sequentially as the data are analyzed and published separately.

Data Transformation

Mixed methods analysis encompasses the entire process of analyzing and interpreting both quantitative and qualitative data within a single study or a program of research.

This involves selecting appropriate analytic techniques for each strand and implementing integration strategies to merge, connect, or build upon the findings.

Mixed methods data transformation involves converting data from one form to another. This can involve:

• Quantitizing: Transforming qualitative data, such as interview transcripts, into numerical codes or categories for statistical analysis.
• Qualitizing: Converting quantitative data, such as survey responses, into narrative descriptions or themes for qualitative analysis.

Data transformation facilitates merging data and conducting analyses that cut across the quantitative-qualitative divide.

Joint Displays: Visual Aid for Integration

Joint displays are visual representations, such as tables, matrices, figures, or graphs. They bring quantitative and qualitative data together to support interpretation and to draw new insights beyond what either strand achieves alone (Guetterman et al., 2015).

They are particularly helpful in merging data, comparing results, and representing meta-inferences, the novel insights that emerge from integrating the two strands.

Types of Joint Displays:

• Side-by-side joint displays present quantitative and qualitative findings alongside each other for direct comparison. For instance, researchers studying patient experiences might present quantitative satisfaction scores next to qualitative themes from interviews to illuminate both the numerical trends and the nuanced reasons behind them
• Integrated matrix displays arrange data in rows and columns to facilitate the comparison of themes, patterns, and relationships between the two strands. Using color-matching to connect corresponding data points in the display can make it easier to compare the quantitative and qualitative findings
• Visual joint displays use graphs, charts, or other visual elements to enhance the presentation and understanding of integrated findings.

Assessment of Fit and Integrated Interpretation

Assessment of fit of integration involves evaluating the coherence and consistency between the quantitative and qualitative findings. This assessment can reveal three potential outcomes:

• Convergence: Findings from both strands align and support each other, strengthening the validity and credibility of the results.
• Divergence: Findings from the two strands differ, prompting further exploration to understand the reasons behind the discrepancies and potentially revealing new perspectives on the phenomenon under study.
• Expansion: Findings from one strand complement and elaborate on the other, providing a broader and more nuanced understanding of the research problem.

Integrated interpretation involves synthesizing the quantitative and qualitative findings to develop a holistic understanding of the research problem, acknowledging both points of convergence and divergence.

This process requires integrated thinking. It is a mindset that values both approaches equally and seeks a synergistic understanding that transcends the limitations of either method in isolation.

Steps for conducting mixed methods research

Remember that mixed methods research is an iterative process. Researchers should remain flexible and adaptable throughout the study, adjusting plans as needed based on emerging findings or unexpected challenges.

The dynamic interplay between quantitative and qualitative approaches is a hallmark of mixed methods research. Embracing this fluidity adds richness and depth to the findings.

Step 1: Formulating the Research Problem

• Identify the Overall Aim: Begin by clearly defining the overarching, long-term goal of the study.
• Develop Research Objectives: Establish specific objectives that will contribute to achieving the overall aim.
• Determine the Research/Mixing Rationale: Articulate a clear rationale for conducting the study, justifying why it is needed. Additionally, explain why mixing quantitative and qualitative approaches is the most appropriate methodology for addressing the research problem. This involves outlining the specific reasons for combining the two approaches, such as triangulation, complementarity, or development.
• Establish the Research/Mixing Purpose: Define the purpose of the study, specifying what will be undertaken. Similar to the rationale, elaborate on the purpose of mixing quantitative and qualitative approaches, explaining how the integration will provide a more comprehensive understanding of the research problem.
• Formulate Research Questions: Develop clear and concise research questions that will guide the study. In mixed methods research, it is essential to include integrated mixed methods research questions that reflect the combined quantitative and qualitative strands.

Step 2: Designing the Study

• Select a Mixed Methods Design: Determine the most suitable mixed methods design based on the research questions, rationale, and purpose. Consider whether a convergent, sequential, transformative, or multiphase design aligns best with the study’s objectives.
• Develop a Sampling Design: Define the target population and create a sampling scheme for both the quantitative and qualitative strands. Specify the sample size for each strand and address any sampling considerations specific to the chosen mixed methods design, such as the use of the same sample, a subsample, multiple samples, or multilevel samples.
• Plan for Data Analysis: Determine the data analysis techniques that will be used for both quantitative and qualitative data. Consider how the data from each strand will be integrated and analyzed to answer the mixed methods research questions.

Collins, Onwuegbuzie, and Jiao (2007) distinguish four mixed methods sampling designs to guide this choice.

• Identical sampling: the same participants take part in both strands. This suits convergent and embedded designs.
• Parallel sampling: different participants from the same population are used for each strand, when using the same people twice is unnecessary or impractical.
• Nested sampling: one strand’s participants are a subset of those sampled for the other. This is typical of sequential designs.
• Multilevel sampling: each strand draws on a different population, to compare how distinct groups experience the same phenomenon.

Step 3: Implementing the Study

• Collect the Data: Gather data using the selected methods, ensuring rigor and adherence to ethical considerations for both quantitative and qualitative data collection.
• Analyze the Data: Analyze the quantitative and qualitative data using the chosen techniques.

Step 4: Integrating and Interpreting Findings

• Validate the Data: Assess the validity and trustworthiness of both the quantitative and qualitative data, employing appropriate methods for each strand.
• Interpret the Data: Interpret the findings from both strands, considering the integrated mixed methods perspective.
• Draw Inferences: Integrate the quantitative and qualitative findings to generate meta-inferences that provide a comprehensive understanding of the research problem. Clearly articulate the insights gained from mixing methods and how the integrated findings contribute to the study’s overall conclusions.
• Meta-Inferences: These are the overarching conclusions drawn by synthesizing findings from the qualitative and quantitative strands.
• Disseminate Findings: Communicate the findings in a clear and concise manner, emphasizing the value added by using a mixed methods approach.

Key Considerations for Integration

• Planning for Integration: Thoughtfully plan for integration throughout the research process, from identifying data sources to selecting integration strategies and planning data analysis.
• Matching Data Sources: In convergent designs, strive to match data sources to ensure that the quantitative and qualitative data capture parallel concepts. This facilitates a more robust integration and comparison of findings.
• Selecting Integration Strategies: Choose appropriate integration strategies, such as merging, connecting, or building, based on the research questions and design. Merging involves combining data to identify convergence, divergence, or relationships. Connecting uses findings from one strand to inform the other. Building develops new insights or hypotheses based on the combined data.
• Representing Integration: Clearly represent and write about the integration process and findings, using tables, figures, or detailed descriptions to illustrate how the quantitative and qualitative strands were combined to generate a more nuanced understanding of the research problem.

Mixed Methods Quality and Publication

The quality of a mixed methods study hinges on several factors:

• Methodological quality: This refers to the rigor and appropriateness of the methods used in both the quantitative and qualitative strands.
• Reporting quality: This pertains to the clarity, transparency, and completeness of the research report in describing the research process, including the integration procedures and the rationale for mixing methods.
• Quality of integration: This refers to the effectiveness of the integration process in generating meaningful and insightful findings that go beyond what either method could achieve alone.

Judging each strand on its own terms means applying two different sets of criteria, not one relaxed compromise. The quantitative strand is conventionally judged on internal validity, external validity (generalizability), reliability, and objectivity.

Lincoln and Guba (1985) proposed four parallel criteria so the qualitative strand is not judged by standards it was never designed to meet.

• Credibility: how believable the interpretation is, given the depth of information gathered and how openly possible sources of bias are acknowledged.
• Transferability: whether the context is described in enough detail for readers to judge if findings apply elsewhere. Purposive samples are not meant to generalize statistically.
• Dependability: whether the researcher has documented the contextual factors that shaped data collection and analysis, since qualitative findings are not expected to repeat perfectly.
• Confirmability: whether procedures are documented well enough for another researcher to follow the analytic trail and check that the data support the conclusions.

Judging the qualitative strand by internal validity and reliability alone applies the wrong yardstick. Half the data would be judged unfairly.

Mixed methods publications often use a weaving approach, which integrates findings throughout the manuscript. Others use a contiguous approach, presenting quantitative and qualitative results in separate sections linked by cross-referencing and discussion.

A staged approach involves publishing multiple papers, each focusing on a specific aspect of the mixed methods study.

Applications of Mixed Methods Research

Mixed methods designs are not confined to any one field. The reasons for combining the two kinds of data recur across disciplines: to explain, to corroborate, to develop an instrument, or to extend scope.

Health Services, Policy, and Program Evaluation

Health services research was an early and enthusiastic adopter of mixed methods. Effectiveness questions ask whether an intervention worked. Implementation questions ask why it worked in one place and not another, and what receiving it felt like.

O’Cathain, Murphy, and Nicholl (2008b) reviewed the quality of published mixed methods studies in this literature. They found the approach well established but unevenly executed.

Evaluators adopted mixed methods for much the same reason. Policy and program decisions need to know not only whether an intervention changed an outcome, but for whom, under what conditions, and why.

Caracelli and Greene (1997) set out an early framework for crafting mixed-method evaluation designs. It distinguishes questions that call for largely independent, component-style designs from those that need genuinely integrated ones. Recent innovations appear under Contemporary Research below.

Education and Clinical Psychology

Education is a natural home for mixed methods. Test scores, attendance, or behavioral incident counts can be connected with the views of students, teachers, and families.

• Special education: Collins, Onwuegbuzie, and Sutton (2006) set out a rationale-and-purpose framework for mixing methods in special education research.
• Bullying: Roques et al. (2020) studied the traumatic experiences and vulnerability of adolescent victims of bullying.

Within clinical psychology, mixed methods help to develop measurement tools and to understand treatment engagement. Three studies show how.

• Measurement: Neto, Baptista, and Dent-Brown (2015) used qualitative case material to build and validate a quantitative system of assimilation indices that tracks change in psychotherapy.
• Treatment dropout: O’Keeffe et al. (2019) combined outcome and interview data to identify different types of therapy dropout among adolescents with depression.
• Trauma coping: Roysircar, Thompson, and Geisinger (2019) paired numerical screening measures with community-level qualitative data on mothers and children in a resource-limited setting in Haiti.

Positive, Community, and Forensic Psychology

Mixed methods suit constructs that resist reduction to a single number, such as grit, hope, and wellbeing. A scale score can be paired with an account of what the construct means to the person reporting it.

• Grit and career success: Clark and Plano Clark (2019) paired quantitative measurement with qualitative narrative.
• Hope and poverty: Clark and Stubbeman (2021) used a comparable design to capture the meaning of hope as well as its magnitude.

In forensic settings, mixed methods can explain a puzzling pattern by returning to the people involved. Campbell, Fehler-Cabral, Bybee, and Shaw (2017) combined case-file data on sexual assault kit submission rates with qualitative interviews.

The interviews explained the institutional and interpersonal reasons why kits were not being submitted for DNA forensic testing.

Benefits of mixed methods research

1. Enhanced Understanding: Mixed methods research provides a more complete comprehension of research problems by combining quantitative and qualitative approaches. This approach is especially valuable in positive psychology, where constructs often have reciprocal relationships, and in understanding complex processes and systems in health and healthcare. For instance, in studying adolescent bullying, a mixed methods approach allows researchers to explore risk factors, PTSD symptoms, and individual experiences.
2. Increased Validity: Using multiple methods can strengthen the validity of findings by allowing triangulation, where qualitative and quantitative data are compared to corroborate results. For example, a study on domestic violence among gay and bisexual men used a convergent design, matching semistructured interviews with survey data to enhance the validity of their findings.
3. Complementarity: Mixed methods research allows researchers to examine different facets of a research question using the strengths of each approach. Quantitative methods can identify trends and relationships, while qualitative methods provide nuanced insights. This approach is beneficial when a single method cannot fully capture the complexity of a phenomenon, such as language learning motivation.
4. Development and Refinement: Researchers can leverage mixed methods research to develop and refine research instruments and interventions. For instance, qualitative data can inform the creation of quantitative surveys, ensuring they are culturally relevant and address specific research questions.
5. Explanation of Findings: Qualitative data can be particularly useful in explaining and contextualizing quantitative results. This allows researchers to move beyond statistical associations and gain a deeper understanding of the underlying mechanisms and individual experiences.
6. Methodological Innovation: Mixed methods research encourages methodological innovation by blending different research traditions. It promotes flexibility and allows researchers to adapt their approaches to best suit their research questions. This is especially valuable for exploring novel research areas or addressing complex social issues.

Disadvantages of mixed methods research

1. Labor Intensity: Mixed methods research demands significant time, resources, and effort compared to single-method studies. The integration of qualitative and quantitative approaches necessitates expertise in designing and implementing both phases, potentially posing challenges for researchers with a predominant quantitative or qualitative orientation.
2. Expertise Requirements: Conducting rigorous mixed methods research requires researchers to have expertise in both quantitative and qualitative methodologies. This can be challenging, as researchers often specialize in one approach. The lack of expertise in either strand can compromise the rigor of the study and lead to methodological concerns, particularly in the qualitative strand. Studies of mixed methods teams also find genuine interdisciplinary collaboration harder to sustain than the design literature implies. Members trained in one tradition often defer to, rather than engage with, colleagues from the other (O’Cathain et al., 2008a).
3. Potential for Bias: The inherent differences between quantitative and qualitative data can make integration challenging. Ensuring that data transformations are defensible and addressing potential biases between methods is crucial for drawing valid inferences.
4. Integration Challenges: Achieving meaningful integration of quantitative and qualitative data can be difficult. The lack of clear guidelines and the potential for irreconcilable data sources can hinder the synergistic potential of mixed methods research, sometimes leading to separate publications of quantitative and qualitative results instead of a unified, integrated analysis. Bazeley (2018) describes this as the gap between studies that are merely mixed and studies that are genuinely integrated.
5. Limited Consensus on Terminology and Quality Assessment: The field of mixed methods research lacks a universally agreed-upon terminology, leading to ambiguity and challenges in comparing and evaluating studies. The absence of standardized quality assessment criteria further complicates the evaluation of rigor and trustworthiness in mixed methods research (Dellinger & Leech, 2007; Fàbregues & Molina-Azorín, 2017).
6. Power Imbalances in Data Integration: Integrating data from samples with different power dynamics, such as those at different levels of a bureaucracy, can raise concerns about the validity of triangulation and the interpretation of findings. The potential for power imbalances to influence the identification of differences or paradoxes necessitates careful consideration during data integration.

Contemporary Research

Since 2015, methodological work has examined how rigorously mixed methods studies are reported, how evaluators combine data in applied research, and where the approach is spreading. Reviews supply the strongest evidence. Their verdict is more cautious than the growth in publishing suggests.

Rigor in Reporting and Appraisal

Brown, Elliott, Leatherdale, and Robertson-Wilson (2015) reviewed 23 peer-reviewed mixed methods studies of school-based obesity interventions, found through a systematic search of five databases. They appraised each against published guidelines for reporting mixed methods studies.

Reporting was often thin. The studies needed more detailed description of data collection, analysis, integration, and inferences, and clearer justification for using mixed methods.

Fàbregues and Molina-Azorín (2017) reviewed approaches to appraising quality across the wider mixed methods literature. They concluded that the field still lacks one widely agreed framework for judging quality. They set out an agenda for shared appraisal criteria.

Both are reviews rather than single studies, so they carry more weight than one small sample. Together they suggest that the gap between the ideal and everyday practice has not closed as the approach has become more popular.

Innovations in Evaluation

Palinkas, Mendon, and Hamilton (2019) reviewed a fast-developing sub-field. The review is only as current as the studies it could draw together when it was written.

Grounded Theory in Mixed Methods

Shim, Johnson, Bradt, and Gasson (2021) built the iterative, constant-comparative logic of grounded theory into a mixed methods framework.

They argued that its theory-building orientation suits the iterative process of mixed methods. Combining the two yields more refined theoretical models than either tradition alone.

Why Psychology Underuses Mixed Methods

Syed and Westberg (2025) address quantitatively trained psychologists. They argue that psychology’s long-standing preference for quantitative methods constrains the questions it is willing to ask. Some questions need words, not numbers.

The review covers the philosophical grounds for combining the two traditions and what distinguishes quantitative from qualitative data. It also maps four major designs onto the questions psychologists already ask. Its aim is to correct researchers’ lack of exposure to qualitative methods.

A second problem emerges. Read alongside the rigor reviews, the review suggests psychology reaches for mixed methods less often than its own questions would justify.

Open Questions

One clear need is methodological. Fàbregues and Molina-Azorín (2017) identified the lack of an agreed quality-appraisal framework for mixed methods systematic reviews. Reviewers still lack a validated tool for rating a mixed methods study’s rigor, unlike trials or qualitative studies.

A second need is substantive. Rosenthal et al. (2025) scoped 41 qualitative studies of adults with ADHD. They named comparisons that a mixed methods design is well placed to resolve.

• Self versus others: reconciling how adults with ADHD describe their own functioning with how other informants rate it.
• Treatment decisions: understanding what drives willingness to start or continue treatment.
• Under-sampled groups: closing evidence gaps for women, participants from diverse racial and ethnic groups, and middle-aged and older adults.

Naming the comparison a design should resolve, rather than simply calling for more qualitative research, is a concrete direction that the mixed methods literature rarely offers.

Philosophical Foundations: The Paradigm Debate

Mixed methods research sits inside a long-running argument about whether quantitative and qualitative approaches can be combined at all. The answer depends on the philosophy behind each.

Purely Quantitative vs. Purely Qualitative Research

Each single-strand tradition rests on its own assumptions. Mixed methods designs try to balance them.

	Purely quantitative	Purely qualitative
Evidence	Numbers, reported as statistics such as means, correlations, effect sizes, and p-values	The meanings participants attach to their experience, analyzed thematically or interpretively
Typical methods	Experiments, surveys, and structured observation	Interviews, focus groups, and observation
Philosophical home	(Post)positivism: a single, knowable reality approximated through objective measurement	Interpretivism or social constructionism: reality is constructed through meaning-making
Strength	Precision, replication, and generalization from a representative sample	Depth, richness, and sensitivity to context and process
Limitation	Shows that a relationship exists without explaining why, for whom, or what the change involved	Small, purposive samples limit generalization, and findings are harder to compare across studies

The quantitative limitation is the one mixed methods designs are built to address. A statistic can show that an intervention reduced depression scores without showing why it worked.

Pragmatism and Its Alternatives

Incompatibility vs. Pragmatism

For much of the late twentieth century, many methodologists treated quantitative and qualitative research as expressions of incompatible worldviews that could not legitimately be combined in one study. This view is called the incompatibility thesis.

Johnson and Onwuegbuzie (2004) set out the alternative that became the field’s dominant justification: pragmatism. Researchers should choose methods for their practical fit to the research question, rather than first resolving the epistemological dispute.

For Morgan (2007), methodology is a set of practical, question-driven choices, not a battleground between rival paradigms. That stance let mixed methods become a distinct tradition.

Critics counter that treating the dispute as a solved, practical non-issue can let a study avoid real tensions. A positivist-leaning measure and an interpretivist-leaning account may claim to represent different things. The assessment of fit step guards against this by treating divergence as a finding, not a nuisance.

Dialectical and Transformative Stances

Pragmatism is not the only response.

Greene, Caracelli, and Graham (1989) laid groundwork for a dialectical stance. Rather than resolving the tension between quantitative and qualitative assumptions, this stance engages with it. The friction between paradigms can generate insights that a tidied-up pragmatism might smooth over.

Mertens (2007) proposed the transformative paradigm. It organizes mixed methods designs around social justice aims and the perspectives of marginalized groups. On this view, method choice should reflect whose interests the research serves, not only which combination of methods best answers the question.

Triangulation: An Older, Narrower Rationale

Combining quantitative and qualitative data is older than mixed methods as a named field. Denzin (1978) distinguished four types of triangulation.

• Data triangulation: drawing on more than one data source, such as different times, settings, or respondents.
• Investigator triangulation: using more than one researcher to collect or analyze the data, so a finding does not rest on one person’s judgment.
• Theory triangulation: interpreting the same data through more than one theoretical lens.
• Methodological triangulation: combining qualitative and quantitative methods to examine the same phenomenon. This is the type most relevant here.

Denzin argued that the strongest designs combine types. Two independent coders analyzing interview data that is then compared against a survey, for example, combine methodological and investigator triangulation.

Jick (1979) gave an early, influential account of triangulation in organizational research, borrowing the navigational metaphor. Converging different methods on the same phenomenon raises confidence that a finding is real rather than an artifact of one method.

Triangulation is narrower than full integration.

A study can triangulate two methods to cross-check one finding without committing to integration at the design, methods, and interpretation levels.

References

Bazeley, P. (2018). Integrating analyses in mixed methods research. SAGE.

Brown, K. M., Elliott, S. J., Leatherdale, S. T., & Robertson-Wilson, J. (2015). Searching for rigour in the reporting of mixed methods population health research: A methodological review. Health Education Research, 30(6), 811–839.

Capon, H., O’Shea, M., Evans, S., & McIver, S. (2021). Yoga complements cognitive behaviour therapy as an adjunct treatment for anxiety and depression: Qualitative findings from a mixed-methods study. Psychology and Psychotherapy: Theory, Research and Practice, 94(4), 1015–1035.

Caracelli, V. J., & Greene, J. C. (1997). Crafting mixed-method evaluation designs. New directions for evaluation, 74, 19-32.

Collins, K. M. T., Onwuegbuzie, A. J., & Jiao, Q. G. (2007). A mixed methods investigation of mixed methods sampling designs in social and health science research. Journal of Mixed Methods Research, 1(3), 267–294.

Collins, K. M., Onwuegbuzie, A. J., & Sutton, I. L. (2006). A model incorporating the rationale and purpose for conducting mixed-methods research in special education and beyond. Learning disabilities: a contemporary journal, 4(1), 67-100.

Creamer, E. G. (2018). Striving for methodological integrity in mixed methods research: The difference between mixed methods and mixed-up methods. Journal of Engineering Education, 107(4), 526–530.

Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.

Dellinger, A. B., & Leech, N. L. (2007). Toward a unified validation framework in mixed methods research. Journal of Mixed Methods Research, 1(4), 309–332.

Denzin, N. K. (1978). The research act: A theoretical introduction to sociological methods (2nd ed.). McGraw-Hill.

Fàbregues, S., & Molina-Azorín, J. F. (2017). Addressing quality in mixed methods research: A review and recommendations for a future agenda. Quality & Quantity, 51(6), 2847–2863.

Fetters, M. D. (2019). The mixed methods research workbook: Activities for designing, implementing, and publishing projects (Vol. 7). Sage Publications.

Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs—principles and practices. Health services research, 48(6pt2), 2134-2156.

Francois-Walcott, R. R. R., Perks, R. E., Vowels, L. M., & Carnelley, K. B. (2024). Insecure attachment and support-seeking during COVID-19: A sequential mixed methods investigation. Attachment & Human Development, 26(2), 159–180.

Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274.

Guetterman, T. C., Fetters, M. D., & Creswell, J. W. (2015). Integrating quantitative and qualitative results in health science mixed methods research through joint displays. The Annals of Family Medicine, 13(6), 554-561.

Hesse-Biber, S., & Johnson, R. B. (2013). Coming at things differently: Future directions of possible engagement with mixed methods research. Journal of Mixed Methods Research, 7(2), 103–109.

Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4), 602–611.

Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.

Mertens, D. M. (2007). Transformative paradigm: Mixed methods and social justice. Journal of Mixed Methods Research, 1(3), 212–225.

Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and quantitative methods. Journal of Mixed Methods Research, 1(1), 48–76.

Neto, D. D., Baptista, T. M., & Dent‐Brown, K. (2015). Development and validation of a system of assimilation indices: A mixed method approach to understand change in psychotherapy. British Journal of Clinical Psychology, 54(2), 147-162.

O’Cathain, A., Murphy, E., & Nicholl, J. (2008a). Multidisciplinary, interdisciplinary, or dysfunctional? Team working in mixed-methods research. Qualitative Health Research, 18(11), 1574–1585.

O’Cathain, A., Murphy, E., & Nicholl, J. (2008b). The quality of mixed methods studies in health services research. Journal of Health Services Research & Policy, 13(2), 92–98.

Palinkas, L. A., Mendon, S. J., & Hamilton, A. B. (2019). Innovations in mixed methods evaluations. Annual review of public health, 40, 423-442.

Rosenthal, E. A., Mitchell, J. T., Weisner, T. S., Silverstein, N., Yi, C., Arnold, L. E., Hechtman, L. T., Hinshaw, S. P., & Jensen, P. S. (2025). What can adults with ADHD tell us about their experiences? A review of qualitative methods to map a new research agenda. Journal of Attention Disorders, 29(13), 1190–1212.

Shannon-Baker, P. (2016). Making paradigms meaningful in mixed methods research. Journal of Mixed Methods Research, 10(4), 319–334.

Shim, M., Johnson, B., Bradt, J., & Gasson, S. (2021). A mixed methods–grounded theory design for producing more refined theoretical models. Journal of Mixed Methods Research, 15(1), 61-86.

Syed, M., & Westberg, D. W. (2025). Mixed-methods research in psychology: Rationales and research designs. Advances in Methods and Practices in Psychological Science, 8(2), Article 25152459251343919.

Examples

Bacchus, L. J., Buller, A. M., Ferrari, G., Brzank, P., & Feder, G. (2018). “It’s always good to ask”:
A mixed methods study on the perceived role of sexual health practitioners asking gay and bisexual men about experiences of domestic violence and abuse. Journal of Mixed Methods Research, 12(2), 221–243.

Campbell, R., Fehler-Cabral, G., Bybee, D., & Shaw, J. (2017). Forgotten evidence: A mixed methods study of why sexual assault kits (SAKs) are not submitted for DNA forensic testing. Law and Human Behavior, 41(5), 454–467.

Clark, R. S., & Plano Clark, V. L. (2019). Grit within the context of career success: A mixed methods study. International Journal of Applied Positive Psychology, 4(3), 91-111.

Clark, R. S., & Stubbeman, B. L. (2021). “I had hope. I loved this city once.”: A mixed methods study of hope within the context of poverty. Journal of Community Psychology, 49(5), 1044-1062.

O’Keeffe, S., Martin, P., Target, M., & Midgley, N. (2019). ‘I just stopped going’: A mixed methods investigation into types of therapy dropout in adolescents with depression. Frontiers in Psychology, 10, Article 75.

Roques, M., Laimou, D., Camps, F. D., Mazoyer, A. V., & El Husseini, M. (2020). Using a mixed-methods approach to analyze traumatic experiences and factors of vulnerability among adolescent victims of bullying. Frontiers in psychiatry, 10, 890.

Roysircar, G., Thompson, A., & Geisinger, K. F. (2019). Trauma coping of mothers and children among poor people in Haiti: Mixed methods study of community-level research. American Psychologist, 74(9), 1189–1206.

Journals

• The Journal of Mixed Methods Research (JMMR) 
• Quality and Quantity
• The Annals of Mixed Methods Research

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