Mixed Methods Study Design: Integrating Quantitative and Qualitative Approaches

Mixed methods study design combines quantitative and qualitative data collection and analysis within a single research project to address complex research questions that neither approach alone can fully answer. For students, researchers, and life-science professionals, this design offers a structured way to capture both the breadth of measurable patterns and the depth of contextual meaning. This article explains the core mixed methods designs, how integration works at each stage of a study, and practical steps for planning data collection and analysis. The guidance draws on peer-reviewed methodology literature and established research infrastructure, including the EQUATOR Network reporting resources and the NC3Rs Experimental Design Assistant.

What Defines a Mixed Methods Study

A mixed methods study deliberately combines quantitative and qualitative approaches in one project. Quantitative research tests hypotheses derived from existing theory using measurable data and deductive reasoning. Qualitative research raises questions, gathers narrative or observational data, and builds theory through inductive analysis. When researchers combine these approaches, they apply mixed methods research to investigate complex phenomena in detail.

The defining feature is not simply collecting both types of data. It is the integration of those data at specific points in the study. Integration can occur at the design level, the methods level, and the interpretation and reporting level. At the design level, researchers choose a structure such as exploratory sequential, explanatory sequential, or convergent design. At the methods level, integration happens through connecting, building, merging, or embedding. At the interpretation level, researchers use narrative synthesis, data transformation, or joint displays to bring findings together.

Mixed methods research is particularly valuable for investigating complex processes and systems. In health services research, for example, it provides powerful tools for understanding how interventions work in real-world settings. The approach helps researchers leverage the strengths of both quantitative and qualitative traditions while compensating for the limitations of each.

Core Mixed Methods Designs

Three basic designs form the foundation of mixed methods research. Each design differs in the timing of data collection, the priority given to each type of data, and the point at which integration occurs.

Convergent Design

In a convergent design, also called convergent parallel design, researchers collect quantitative and qualitative data concurrently and analyze them separately before merging the results. The purpose is to obtain complementary views of the same phenomenon and compare findings for convergence or divergence.

This design works well when you have equal priority for both types of data and want to validate findings across methods. For example, a study of student learning in a gamified calculus course used a pretest-posttest quasi-experimental design combined with qualitative interviews. The quantitative data measured learning performance while the interviews explored student perceptions of motivation and autonomy. The qualitative findings helped explain why the quantitative results showed no significant effects for the mediating variables.

A convergent design requires careful planning to ensure both data collection streams address the same research questions. The analysis phase brings the two datasets together, often through comparison matrices or joint displays.

Explanatory Sequential Design

The explanatory sequential design begins with quantitative data collection and analysis, followed by qualitative data collection that helps explain or elaborate on the quantitative findings. The notation for this design is quan to QUAL, indicating that the qualitative phase carries more weight in interpretation.

This design is useful when you have identified a pattern or relationship in quantitative data and need to understand the mechanisms behind it. A study of sexually transmitted disease among street dwellers in southern Ethiopia used this approach. The quantitative phase surveyed 842 respondents about knowledge, attitudes, and treatment-seeking behaviors. The qualitative phase used in-depth interviews and key informant interviews to explore reasons for not seeking care. The qualitative findings revealed that fear of questions raised by providers was a barrier to treatment, a finding that the survey alone could not capture.

Another application used extreme case selection within an explanatory sequential framework. County-level colorectal cancer mortality-to-incidence ratios identified high-performing and low-performing places. These quantitative contrasts informed purposive selection of community sites for focus groups and provider interviews. This approach moves from population-level description to contextually grounded explanation.

Exploratory Sequential Design

The exploratory sequential design reverses the order. Qualitative data collection and analysis come first, followed by quantitative data collection. The notation is QUAL to quan, with the qualitative phase carrying more weight.

This design is appropriate when you need to explore a phenomenon with little existing theory or measurement tools. The qualitative phase helps identify relevant factors, develop hypotheses, or create instruments. The quantitative phase then tests or measures those factors with a larger sample.

A study of reablement implementation used this design. The researchers conducted a literature search followed by framework analysis using the Consolidated Framework of Implementation Research domains. This qualitative work identified 40 potentially relevant factors. A survey was then developed from these factors and administered to international experts who rated their relevance. The quantitative phase confirmed that 35 factors were relevant, with teamwork and communication skills rated most highly.

An exploratory mixed methods study of occupational accidents in palm oil workers also used this approach. Quantitative data from 191 workers identified candidate risk factors through descriptive and bivariate analyses. Qualitative data from 12 informants provided contextual insights that informed variable selection and interpretation. The integration revealed that workload-related fatigue, barriers to personal protective equipment use, and normalization of hazardous conditions contributed to risk beyond the statistically significant variables.

Advanced Mixed Methods Frameworks

Beyond the three basic designs, four advanced frameworks extend mixed methods integration for complex research contexts.

Multistage Design

A multistage design uses multiple phases of data collection and analysis, often alternating between quantitative and qualitative approaches. Each phase builds on the previous one. This design suits longitudinal studies or program evaluations where research questions evolve over time.

Intervention Design

An intervention design embeds mixed methods within an intervention study, such as a randomized controlled trial. The quantitative component measures intervention effects while the qualitative component explores implementation processes, participant experiences, or contextual factors. A study of trailbridges in rural Rwanda used a stepped-wedge randomized controlled trial with 147 sites, supplemented by three qualitative sub-studies investigating bridge use patterns, market prices, and spatial extent of impacts.

Case Study Design

A case study design applies mixed methods within a bounded case or set of cases. The convergent parallel design mixed methods case study in problem-based learning exemplifies this approach. Researchers examine a case in depth using both quantitative and qualitative data to understand the case holistically.

Participatory Design

A participatory design involves stakeholders in the research process. Community members or service users contribute to question formulation, data collection, interpretation, or dissemination. This approach promotes reflection, collective discussion, and community empowerment, particularly in place-based health research.

Integration at the Methods Level

Integration at the methods level occurs through four approaches. Each approach links the quantitative and qualitative databases in a different way.

Connecting

Connecting occurs when one database links to the other through sampling. For example, in an explanatory sequential design, the quantitative results identify participants for qualitative follow-up interviews. Extreme case selection is a form of connecting where quantitative outliers or extreme cases are purposively selected for qualitative inquiry.

Building

Building occurs when one database informs the data collection approach of the other. In an exploratory sequential design, the qualitative findings inform the development of a survey instrument or intervention. The reablement study used building when the framework analysis generated factors that became survey items.

Merging

Merging brings the two databases together for analysis. This approach is common in convergent designs where quantitative and qualitative data are analyzed separately and then compared. Joint displays are a visual tool for merging data and drawing out new insights.

Embedding

Embedding links data collection and analysis at multiple points throughout the study. This approach is common in intervention designs where qualitative data are collected before, during, and after the quantitative intervention to capture process and outcome data.

Integration at the Interpretation and Reporting Level

Integration continues after data analysis through three approaches at the interpretation and reporting level.

Narrative Integration

Narrative integration weaves the quantitative and qualitative findings together in a coherent account. The weaving technique describes findings from both datasets in a single narrative, organized by theme or research question. A sequential explanatory mixed methods study of midwives knowledge and confidence used this approach to present integrated findings.

Data Transformation

Data transformation converts one type of data into another to facilitate comparison. Qualitative themes can be quantified as frequencies or categories. Quantitative variables can be qualitized into narrative descriptions. This approach enables statistical and thematic analyses of the same underlying phenomenon.

Joint Display

A joint display is a visual tool that brings quantitative and qualitative data together in a single table, figure, or matrix. Joint displays provide a structure to discuss integrated analysis and help researchers and readers understand how mixed methods yields new insights. The most prevalent types of joint displays are statistics-by-themes and side-by-side comparisons. Innovative joint displays connect findings to theoretical frameworks or recommendations.

Researchers have used joint displays for convergent, explanatory sequential, exploratory sequential, and intervention designs. Exemplar joint displays represent mixed methods integration, present integrated results, and yield new insights beyond what either dataset provides alone.

Fit of Integration

The fit of integration describes the extent to which the qualitative and quantitative findings cohere. Three types of fit are possible.

Confirmation

Confirmation occurs when the qualitative and quantitative findings agree and support each other. In a study of safety culture in healthcare, the joint analysis of quantitative survey data and qualitative focus group data showed that the information and reflections of participants supported and validated the statistical analysis results.

Expansion

Expansion occurs when the findings diverge but together provide a broader understanding. The qualitative findings may explain unexpected quantitative results or reveal additional dimensions. In the gamification study, the qualitative analysis revealed themes of controlled motivation that the quantitative measures did not capture, expanding the interpretation of the learning performance results.

Discordance

Discordance occurs when the findings contradict each other. Discordant findings require careful examination. They may indicate measurement problems, sampling issues, or genuinely different aspects of the phenomenon. Researchers should report discordance transparently and explore possible explanations.

Selecting a Mixed Methods Design

Selecting an appropriate design requires matching the research question, the state of existing knowledge, and the practical constraints of the study context.

Research Question Alignment

The research question determines the appropriate design. If you need to explain quantitative findings with qualitative depth, choose an explanatory sequential design. If you need to develop a measurement tool or theory before quantitative testing, choose an exploratory sequential design. If you need complementary perspectives on the same phenomenon, choose a convergent design.

Priority and Timing Decisions

Decide which type of data carries more weight in answering the research question. The notation system uses uppercase letters for the priority method and lowercase for the secondary method. Quan to QUAL indicates a quantitative first design with qualitative priority. QUAL to quan indicates a qualitative first design with quantitative priority.

Timing decisions involve whether data collection occurs concurrently or sequentially. Concurrent designs save time but require careful management of two data collection streams. Sequential designs allow each phase to inform the next but take longer.

Practical Constraints

Consider the resources available for the study. Mixed methods research requires skills in both quantitative and qualitative methods. Team composition should include members with complementary expertise. Time and budget constraints may favor one design over another. The complexity of the design is associated with the number of aims or objectives, the study context, and the phase of implementation examined.

Planning Data Collection

Planning data collection for a mixed methods study requires attention to sampling, instrumentation, and quality assurance for both types of data.

Sampling Strategies

Quantitative sampling typically aims for representativeness and statistical power. Qualitative sampling aims for information richness and theoretical relevance. Mixed methods studies often use nested or linked sampling where one sample is drawn from the other.

In an explanatory sequential design, the qualitative sample is often a subset of the quantitative sample. Extreme case selection identifies participants or sites with particularly high or low scores on key quantitative variables. In an exploratory sequential design, the quantitative sample may be drawn from a broader population after the qualitative phase identifies relevant constructs.

Instrumentation

Quantitative instruments include surveys, questionnaires, tests, and physiological measures. These instruments should be validated for the study population. Qualitative instruments include interview guides, observation protocols, and document analysis frameworks. These instruments should be flexible enough to capture unexpected findings.

When building from qualitative to quantitative phases, the qualitative findings inform instrument development. The reablement study used framework analysis to generate survey items that were then rated by experts. This building approach ensures the quantitative instrument measures constructs relevant to the study context.

Quality Assurance

Quality assurance for quantitative data includes reliability and validity checks, data cleaning procedures, and appropriate statistical analysis. Quality assurance for qualitative data includes credibility checks, audit trails, and systematic analysis procedures.

The Mixed Methods Appraisal Tool provides criteria for critically appraising the quality of qualitative, survey, and mixed methods studies. The tool was revised through a modified e-Delphi study that identified six qualitative criteria, eight survey criteria, and seven mixed methods criteria. Using such tools during planning can improve study quality.

Analyzing and Integrating Data

Data analysis in mixed methods research proceeds through separate analysis of each dataset followed by integration.

Separate Analysis

Quantitative analysis follows established statistical procedures appropriate to the research questions and data characteristics. Descriptive statistics summarize the sample and key variables. Inferential statistics test hypotheses and estimate relationships. The choice of statistical methods depends on the study design and data distribution.

Qualitative analysis follows established procedures such as thematic analysis, framework analysis, or content analysis. Thematic analysis typically follows a systematic process of coding, category development, and theme identification. The six-phase approach described by Braun and Clarke is widely used for this purpose.

Integration Analysis

Integration analysis brings the two datasets together. The approach depends on the design and the integration points planned in advance.

For convergent designs, integration often involves comparing findings side by side. A comparison matrix lists themes or research questions with corresponding quantitative and qualitative findings. This comparison reveals areas of confirmation, expansion, or discordance.

For sequential designs, integration often involves using the second phase to explain or build on the first phase. In explanatory sequential designs, the qualitative findings provide explanations for quantitative patterns. In exploratory sequential designs, the quantitative findings test or extend qualitative insights.

Joint displays are valuable tools for integration analysis. A statistics-by-themes display presents quantitative statistics alongside qualitative themes for each research question. A side-by-side comparison presents the two types of data in adjacent columns. These displays help researchers identify new insights that emerge from the juxtaposition of different data types.

Common Failure Patterns in Mixed Methods Research

Several common failure patterns reduce the quality and utility of mixed methods studies. Recognizing these patterns helps researchers avoid them.

Token Integration

Token integration occurs when both types of data are collected but analyzed and reported separately. The study is labeled mixed methods but the findings are never truly integrated. This failure pattern is common when researchers lack a clear integration plan or when time constraints push analysis into separate tracks.

Prevention requires planning integration at the design stage. Specify how the datasets will be connected, built, merged, or embedded. Identify the integration points in advance and allocate time for integration analysis.

Priority Confusion

Priority confusion occurs when the study design does not clearly specify which method carries more weight. Without clear priority, the analysis may give equal weight to both datasets when the research question requires one to dominate. This confusion can lead to superficial treatment of the priority method.

Prevention requires explicit decisions about priority and timing. Document these decisions in the study protocol. Use the notation system to communicate the design clearly.

Mismatched Samples

Mismatched samples occur when the quantitative and qualitative samples do not align with the research question. For example, a quantitative survey of a broad population may be followed by qualitative interviews with a convenience sample that does not represent the survey respondents. This mismatch limits the explanatory power of the qualitative phase.

Prevention requires linked sampling strategies. In sequential designs, the qualitative sample should be drawn from the quantitative sample using purposive criteria. In convergent designs, both samples should be drawn from the same population with clear inclusion criteria.

Inadequate Reporting

Inadequate reporting occurs when the methods and integration procedures are not described clearly enough for readers to evaluate the study. Readers cannot assess the quality of integration or the validity of the conclusions.

Prevention requires transparent reporting of the design, sampling, data collection, analysis, and integration procedures. Reporting guidelines and appraisal tools provide structure for this reporting.

Quality Standards and Appraisal

Quality standards for mixed methods research continue to develop across disciplines. The Mixed Methods Appraisal Tool provides criteria for appraising the quality of mixed methods studies. The tool was revised through a modified e-Delphi study that identified seven mixed methods criteria. These criteria address the adequacy of the mixed methods design, the integration of data, and the interpretation of integrated findings.

Discipline-specific quality standards also exist. Engineering education research has established quality standards for empirical studies using mixed methods designs. These standards address the rigor of both quantitative and qualitative components and the quality of their integration.

The EQUATOR Network provides reporting guidelines for health research, including guidance relevant to mixed methods studies. Researchers should consult these resources during study planning and reporting.

The NC3Rs Experimental Design Assistant supports researchers in designing rigorous experiments, including considerations relevant to mixed methods studies that include experimental components.

Records and Documentation

Maintaining thorough records is essential for mixed methods research. Documentation supports transparency, reproducibility, and quality appraisal.

Study Protocol

The study protocol documents the research questions, design, sampling strategy, data collection procedures, analysis plan, and integration plan. A pre-analysis plan specifies the planned analyses before data collection begins. The trailbridges study in rural Rwanda presented a study design and pre-analysis plan that specified the stepped-wedge randomized controlled trial and the supplementary qualitative sub-studies.

Data Management

Data management procedures should address both quantitative and qualitative data. Quantitative data require cleaning, validation, and secure storage. Qualitative data require transcription, anonymization, and systematic organization. The Research Data Framework from the National Institute of Standards and Technology provides guidance for managing research data throughout the research lifecycle.

Analysis Documentation

Document the analysis procedures for both datasets. For quantitative analysis, record the statistical tests, assumptions, and software used. For qualitative analysis, record the coding framework, coding decisions, and theme development. For integration, document the integration approach and the evidence for convergence, expansion, or discordance.

Audit Trail

An audit trail documents the research process from design through reporting. This trail supports credibility for qualitative components and transparency for quantitative components. It also supports the evaluation of integration quality.

Practical Implementation Steps

Implementing a mixed methods study requires systematic planning and execution. The following steps provide a practical framework.

Step 1: Define the Research Question

Articulate the research question clearly. Determine whether the question requires both quantitative and qualitative data. Consider whether the question asks about patterns and relationships, mechanisms and meanings, or both.

Step 2: Select the Design

Choose the design that best fits the research question and practical constraints. Use the decision table below to guide this selection.

Step 3: Plan Integration Points

Identify where and how integration will occur. Specify the integration approach at the methods level and the interpretation level. Plan for joint displays or other integration tools.

Step 4: Develop Sampling Strategy

Plan linked sampling that aligns the quantitative and qualitative samples. Specify inclusion criteria, sample sizes, and selection procedures for each phase.

Step 5: Develop and Validate Instruments

Develop or select quantitative instruments with appropriate psychometric properties. Develop qualitative instruments with appropriate flexibility and rigor. If building from qualitative to quantitative phases, ensure the qualitative findings inform instrument development.

Step 6: Collect Data

Implement the data collection plan. Monitor data quality throughout collection. Document any deviations from the plan and their rationale.

Step 7: Analyze Each Dataset

Analyze the quantitative and qualitative data separately using appropriate procedures. Document all analysis decisions.

Step 8: Integrate Findings

Bring the findings together using the planned integration approach. Use joint displays to visualize integration. Identify areas of confirmation, expansion, and discordance.

Step 9: Interpret and Report

Interpret the integrated findings in relation to the research questions and existing literature. Report the study transparently, including the design, methods, integration procedures, and limitations.

At a Glance: Design Selection Table

Common Failure Patterns Table

Limitations of Mixed Methods Research

Mixed methods research has limitations that researchers should acknowledge and address.

Resource Intensity

Mixed methods studies require more time, funding, and expertise than single-method studies. Researchers must be competent in both quantitative and qualitative methods or work in teams with complementary skills. The complexity of design is associated with the number of aims or objectives, the study context, and the phase of implementation examined.

Integration Difficulty

Meaningful integration of qualitative and quantitative data remains challenging. Integration requires deliberate planning and skilled execution. Without adequate integration, the study becomes two separate studies instead of a coherent mixed methods study.

Paradigm Tensions

Quantitative and qualitative research are grounded in different philosophical assumptions. Quantitative research typically follows a deductive approach, testing hypotheses derived from theory. Qualitative research typically follows an inductive approach, building theory from data. Researchers must navigate these tensions pragmatically, focusing on what works for the research question.

Generalizability Constraints

Mixed methods studies often trade breadth for depth. The qualitative component typically uses smaller samples that limit generalizability. The quantitative component may use convenience samples that limit external validity. Researchers should be transparent about these constraints.

Safety and Regulatory Context

Mixed methods research involving human participants must comply with ethical and regulatory requirements. Researchers should obtain appropriate ethics approval before data collection begins.

Informed Consent

Participants must provide informed consent for both quantitative and qualitative components. Consent procedures should address data collection, storage, and use. For qualitative components, consent should address recording and transcription procedures.

Data Protection

Quantitative and qualitative data may contain sensitive information. Researchers must protect participant confidentiality and privacy. Anonymization procedures should be applied to qualitative transcripts and quantitative datasets.

Vulnerable Populations

Studies involving vulnerable populations require additional safeguards. The child-centred research checklist was developed to improve the design and reporting of research with children. This checklist was developed through a descriptive mixed methods study with five sequential phases, including a Delphi component with 158 experts from 18 countries.

Professional Escalation

Researchers should have procedures for escalating concerns identified during data collection. For example, if qualitative interviews reveal safety concerns or distress, researchers should have referral pathways to appropriate services. If quantitative data reveal unexpected risks, researchers should have procedures for reporting and responding.

Professional Escalation Criteria

Researchers should escalate concerns to appropriate authorities or supervisors when specific conditions are met.

Ethical Concerns

Escalate when data collection procedures cause participant distress, when consent procedures are inadequate, or when data protection is compromised. Ethics committees should be notified of any protocol deviations that affect participant welfare.

Data Quality Concerns

Escalate when data quality is seriously compromised, such as systematic missing data, instrument failure, or contamination between study conditions. The study team should assess whether the data can support valid conclusions.

Safety Concerns

Escalate when data collection reveals immediate safety risks to participants or researchers. In occupational health studies, for example, findings of hazardous conditions should be reported to relevant authorities.

Regulatory Concerns

Escalate when the study may violate regulatory requirements. This includes requirements for ethics approval, data protection, or reporting of adverse events.

Frequently Asked Questions

What is the difference between mixed methods and multi-methods research?

Mixed methods research integrates quantitative and qualitative data within a single study, with integration occurring at design, methods, or interpretation levels. Multi-methods research uses multiple methods from the same tradition, such as two different quantitative approaches or two different qualitative approaches. The defining feature of mixed methods is the integration of quantitative and qualitative data to yield insights beyond what either approach provides alone.

How do I choose between convergent and sequential designs?

Choose a convergent design when you need complementary perspectives on the same phenomenon and can collect both types of data concurrently. Choose an explanatory sequential design when you have quantitative findings that need qualitative explanation. Choose an exploratory sequential design when you need to develop theory or instruments before quantitative testing. The research question and the state of existing knowledge should guide this decision.

What does the notation quan to QUAL mean?

The notation indicates the sequence and priority of methods. Uppercase letters indicate the method with more weight in answering the research question. Lowercase letters indicate the secondary method. Quan to QUAL means quantitative data collection comes first, followed by qualitative data collection that carries more interpretive weight. This notation is commonly used for explanatory sequential designs.

How do I integrate quantitative and qualitative data?

Integration occurs through connecting, building, merging, or embedding at the methods level. At the interpretation level, integration occurs through narrative, data transformation, or joint displays. The specific approach depends on your design. For convergent designs, merging and joint displays are common. For sequential designs, connecting through sampling and building through instrument development are common.

What is a joint display in mixed methods research?

A joint display is a visual tool that brings quantitative and qualitative data together in a single table, figure, or matrix. The most prevalent types are statistics-by-themes and side-by-side comparisons. Joint displays help researchers and readers understand how mixed methods provides new insights beyond what either dataset provides alone.

How do I report a mixed methods study transparently?

Report the design, sampling strategy, data collection procedures, analysis procedures, and integration approach clearly. Describe how the datasets were integrated and what the integration revealed. Report areas of confirmation, expansion, and discordance. Use reporting guidelines and appraisal tools to structure your reporting.

What quality appraisal tools are available for mixed methods studies?

The Mixed Methods Appraisal Tool provides criteria for appraising the quality of qualitative, survey, and mixed methods studies. The tool was revised through a modified e-Delphi study that identified seven mixed methods criteria. Discipline-specific quality standards also exist, such as those for engineering education research.

Can mixed methods be used in experimental studies?

Yes. Intervention designs embed mixed methods within experimental studies such as randomized controlled trials. The quantitative component measures intervention effects while the qualitative component explores implementation processes and participant experiences. The trailbridges study in rural Rwanda used a stepped-wedge randomized controlled trial supplemented by qualitative sub-studies.

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References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Achieving integration in mixed methods designs-principles and practices.. Health services research, 2013.
• Quantitative, Qualitative, Mixed Methods, and Triangulation Research Simplified.. Journal of continuing education in nursing, 2024.
• Integrating Quantitative and Qualitative Results in Health Science Mixed Methods Research Through Joint Displays.. Annals of family medicine, 2015.
• Conducting and Writing Quantitative and Qualitative Research.. Journal of Korean medical science, 2023.
• Improving the content validity of the mixed methods appraisal tool: a modified e-Delphi study.. Journal of clinical epidemiology, 2019.
• Safety culture in healthcare: mixed method study.. Journal of health organization and management, 2021.
• Mixed method designs in implementation research.. Administration and policy in mental health, 2011.
• Mixed methods research.. Nursing standard (Royal College of Nursing (Great Britain) : 1987), 2015.
• Integrating epidemiologic analysis and mixed methods design to advance place-based community health research: An explanatory sequential framework using extreme case selection.. 2026.
• STEAM Pedagogy and Student Competency Development: A Comparative Mixed-Methods Study of Institutional Contexts in Pakistan and Japan. 2026.
• Advancing urban exposome research through the integration of qualitative and quantitative approaches in epidemiological research.. 2026.
• Integrated Collaborative Learning and Polya Problem Solving Model for Enhancing Pre Service Teachers Critical Thinking through the Mediating Roles of Student Engagement and Attitudes and the Moderating Role of Prior Knowledge. 2026.
• Integrating first-person smart glasses video into oral and Maxillofacial surgery education: a mixed-methods study on student learning and experience.. 2026.
• An Exploratory Mixed-Methods Study of Risk Factors and Predictive Modelling of Occupational Accidents in Palm Oil Workers. 2026.
• A child-centred research checklist to improve the design and reporting of paediatric research studies: A descriptive mixed methods study.. International Journal of Nursing Studies, 2024.
• Doctoral Colloquium-How Interactivity and Presence Affect Learning in Immersive Virtual Reality: A Mixed Methods Study Design. International Conference on Immersive Learning Research Network, 2024.
• A sequential explanatory mixed methods study design: An example of how to integrate data in a midwifery research project. Journal of Nursing Education and Practice, 2020.
• Mixed methods study design, pre-analysis plan, process evaluation and baseline results of trailbridges in rural Rwanda.. Science of the Total Environment, 2022.
• Reablement - relevant factors for implementation: an exploratory sequential mixed-methods study design. BMC Health Services Research, 2022.
• Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 1966.
• Sexually transmitted disease among street dwellers in southern Ethiopia: a mixed methods study design. BMC Public Health, 2020.
• How gamification boosts learning in STEM higher education: a mixed methods study. International Journal of STEM Education, 2025.
• Thinking qualitatively in the context of mixed methods research: implications for study design. International Encyclopedia of Education Fourth Edition, 2022.
• Convergent parallel design mixed methods case study in problem-Based learning. Proceedings of the European Conference on Research Methods in Business and Management Studies, 2017.
• Establishing Quality Standards for Empirical Studies using Mixed-methods Designs in Engineering Education Research. International Journal of Engineering Education, 2025.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.