the combined use of quantitative and qualitative research methods

Mixed Methods Research

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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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 research question requires a multifaceted approach that can simultaneously explore trends in data and the nuances of individual experiences.

Integration, a key concept in mixed methods research, is the intentional combining of quantitative and qualitative research in such a way that they become interdependent and work together to achieve a common research goal.

For example, examining changes in school funding ( quantitative ) alongside teacher and student narratives ( qualitative ) about educational quality in a school district can provide a more comprehensive understanding of the relationship between funding and the actual experiences of those within the school system.

Several factors make mixed methods research distinct from conducting separate quantitative and qualitative studies. A few of these defining factors are:

• 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: Integration, a key aspect of mixed methods research, involves intentionally combining quantitative and qualitative research to create interdependence and synergy between the two approaches. There are multiple potential levels of integration, including at the design, methods, and representation 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.

Here are some examples of how people use 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:

• Researchers could conduct a study to understand the impact of a new school-based mental health program on student well-being. Qualitative data could be collected through interviews with students and teachers to explore their experiences with the program and identify any barriers or facilitators to implementation. This data could then be used to explain variations in quantitative data on student mental health outcomes, such as changes in depression or anxiety symptoms.
• A mixed methods study could be used to investigate the relationship between patient satisfaction and health outcomes following a specific medical procedure, such as surgery . Qualitative data from patient interviews or focus groups could provide insights into the reasons behind varying levels of satisfaction. Researchers could then connect these qualitative findings with quantitative data on post-surgical complications, recovery time, or readmission rates to see if there are correlations or patterns.
• Researchers could use a mixed methods approach to examine the effectiveness of a teacher training program on student academic achievement in a particular subject, like mathematics. Quantitative data on student test scores before and after the training could be combined with qualitative data from teacher interviews and classroom observations. This integration of data could help determine if improvements in student performance are related to changes in teaching practices resulting from the training.

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.

• 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.
• 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.
• 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.
• 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.
• 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.

Mixed methods research designs

Mixed methods research designs provide researchers with a structured approach to combining qualitative and qualitative data. Creswell and Plano Clark (2018) identify three core mixed methods designs:

Convergent Parallel Design

Convergent parallel design involves simultaneously collecting quantitative and qualitative data, analyzing these datasets separately, and then merging the results for interpretation.

For example, this design could be used to understand domestic violence and abuse among gay and bisexual men by matching data from semi-structured interviews with survey data.

Here’s how it 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.

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 look at survey data and then followed up with interviews to get a deeper understanding of the factors involved.

• 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.
• Then, you take those quantitative results – particularly the ones that are significant, surprising, or need further explanation – and use them 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 is most effective when researchers have a clear rationale for using a mixed methods approach and when the research questions lend themselves to both qualitative and quantitative exploration.

• 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.

An example:

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.

Embedded (or nested) Design

Embedded or Nested Designs in Health Sciences

Embedded designs use quantitative and qualitative approaches together, with one embedded in the other, to yield greater insight. This might look like supplemental qualitative data embedded in a larger quantitative study design, such as an experimental trial. These types of designs may be a variation of convergent or sequential designs.

Embedded design is a method for linking qualitative and quantitative data collection and analysis at multiple points, particularly useful in intervention research.

Qualitative data may be used prior to the intervention to inform strategies to best recruit individuals or to develop the intervention, during the experiment to examine the process being experienced by participants, or after the experiment to follow up and better understand the quantitative outcomes.

Embedding involves combining connecting, building, or 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.
• 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.

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:

• 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.
• 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.
• 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.

By adhering to these principles and employing these strategies, researchers can leverage the strengths of mixed methods research to address complex research questions and generate rich, insightful, and impactful findings.

Joint Displays : Visual Aid for Integration

Joint displays are visual representations, such as tables, matrices, figures, or graphs, that bring together quantitative and qualitative data to facilitate interpretation and draw new insights beyond what each strand could achieve independently.

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, 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, and embracing this fluidity contributes to the richness and depth of the findings.

Step1: 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.

Step3: 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.

Mixed methods publications often employ a weaving approach, integrating findings throughout the manuscript, or a contiguous approach, presenting quantitative and qualitative results in separate sections but linking them through cross-referencing and discussion.

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

Benefits of mixed methods research

• 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.
• 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.
• 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.
• 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.
• 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.
• 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

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

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

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Mixed methods research: combining qualitative and quantitative data.

17 min read Unlock the full potential of your research by integrating the precision of data with the depth of human insight – because in today’s complex world, one method alone just isn’t enough.

Author: Will Webster

Subject Matter Expert: Emily Geisen

What is mixed methods research?

Mixed methods research is a powerful approach that combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a topic.

While quantitative research focuses on numbers and statistical analysis, qualitative research dives into the experiences, opinions and motivations of people. By combining these two approaches, researchers can explore the “what” and the “why”, providing the best of both worlds to deliver richer insights than either method could alone.

This approach is particularly valuable when dealing with complex issues that can’t be fully understood through numbers or words alone. For instance, you might start with a survey to gather broad data on a population (quantitative), and then follow up with interviews or focus groups to explore deeper meanings and nuances (qualitative). By integrating these findings, you can cross-validate results and gain a more well-rounded understanding of the topic or problem you’re exploring.

Qualitative vs. quantitative research

To understand mixed methods studies, it’s crucial to first know the differences between qualitative and quantitative research.

Quantitative research is all about numbers and measurements. Leveraging tools like surveys, it deals with data that can be counted, categorized and statistically analyzed. This approach is great for answering questions like “how many?” or “what proportion?” – when you need to measure something precisely, test hypotheses or identify patterns across large groups.

On the other hand, qualitative research is more about exploring depth and meaning. Instead of numbers, it focuses on words, images and experiences – helping you understand the “how” and “why”. Leveraging methods like interviews, focus groups and open-ended surveys, it’s used when you want to dive deep into people’s thoughts, feelings or behaviors.

Integrating qualitative and quantitative methods

Crucially, in the context of mixed methods research, these two approaches are not in competition – they’re complementary.

Integrating both qualitative and quantitative methods isn’t just about using both approaches in the same study – it’s about strategically combining them in a way that each method enhances and informs the other.

There are several ways to integrate these methods – which we’ll come on to very soon. But no matter how they’re integrated, the goal of mixed methods research is to leverage the strengths of both qualitative and quantitative approaches.

When to use mixed methods research

Mixed methods research is a versatile approach that can be applied at various stages of your research process .

No matter if your goal is to explore a challenging issue, broaden the scope of your insights or dig deeper into your data, the aim is to use the right combination of methods throughout your entire research process to make well-rounded, informed decisions.

Let’s look at the core applications.

Throughout your research journey

Mixed methods research isn’t just a one-time tool – it’s a strategy that can be applied throughout the entire research journey.

Imagine you want to understand a new market or consumer behavior. In the beginning, you might rely entirely on qualitative methods, such as interviews or focus groups, to gather deep, rich insights. As your understanding deepens and you begin to form hypotheses, you can then transition to a more quantitative approach.

By moving from a qualitative study to a quantitative one, you’re not just validating your initial insights – you’re also generalizing them, making them actionable on a broader scale.

This approach is particularly useful for businesses – especially when research is one of many tasks being juggled. It allows you to adapt your research methods over time, aligning them with the evolving needs of your project and the decisions you need to make. Rather than trying to capture everything in a single study, you can spread your research across multiple phases, using the strengths of each method where they’re most needed.

Tackling complex research questions

Complex research questions often require a multi-faceted approach to capture all the relevant dimensions. Mixed methods research excels here by allowing researchers to tackle complexity head-on, with its dual approach ensuring that no aspect of the research question is overlooked.

For instance, you can use qualitative methods to identify the underlying factors of a complex issue, then apply quantitative methods to measure the extent of those factors across a broader population.

Generalizing and expanding qualitative findings

Often it’s not enough to simply understand a topic on a deep level – you also need to know its breadth and how it may apply to a larger audience. This is again where mixed methods research shines.

After gathering in-depth qualitative insights, mixed methods research allows you to use quantitative techniques to test these insights across a broader group – helping you generalize your findings, making them more actionable and relevant to a wider audience.

Explaining quantitative results

Mixed methods research allows you to dig deeper into quantitative results by adding a qualitative layer of understanding. Because while numbers can tell you a lot, they don’t always give the full story.

For instance, if your quantitative data reveals a surprising trend, qualitative methods can help you explore the reasons behind it, helping you explain and contextualize your data. With a mixed methods approach, you can ensure that your quantitative insights aren’t just statistically significant, they’re also both meaningful and relevant.

Benefits of mixed methods research

Mixed methods research brings together the best of both quantitative and qualitative approaches, offering a range of benefits that make it an ideal choice for any researcher or business looking for comprehensive insights.

By integrating these methods, you can ensure that your research is not only thorough but also adaptable to the evolving needs of your projects.

Triangulation and validation

One of the standout advantages of mixed methods research is the ability to triangulate and validate your findings. By using both quantitative and qualitative data, you can cross-check your results, ensuring they hold up under different lenses to strengthen the credibility of your conclusions.

In practice, having all your data in one place simplifies this validation process. Doing everything within a single platform, like Qualtrics® XM for Strategy and Research ™, makes it easier to compare and contrast findings – ensuring that your insights are well-rounded and reliable without the need for complex data management.

Holistic insights and context

Mixed methods research excels at providing holistic insights.

Quantitative data gives you the big picture; qualitative data fills in the gaps. This combination results in a richer, more nuanced understanding of your research subject.

When all your data is accessible and integrated, drawing connections between the broader trends and the detailed stories becomes more straightforward, empowering you to build a more complete and contextually rich narrative to help you make more informed decisions.

Flexibility in research design

Mixed methods research is flexible by definition – allowing you to adapt your research design as new questions or challenges arise.

Whether you start with a qualitative exploration to define the scope of your research, or you begin with quantitative analysis and follow up with qualitative methods to delve deeper, this flexibility is especially beneficial in fast-paced environments where the ability to pivot quickly can be a huge competitive advantage.

A single platform that supports both quantitative and qualitative research enhances this flexibility. With the right tool you can easily switch between methods, integrate new data and refine your approach without having to juggle multiple tools or platforms.

With a single platform you’re not only saving time, you’re also ensuring that your research remains cohesive and aligned with your overall objectives.

Types of mixed methods research designs

Mixed methods research isn’t a one-size-fits-all approach; there are several designs you can choose from depending on your research goals and the nature of your study.

Each design offers a different way to integrate quantitative and qualitative methods, allowing you to tailor your research process to best address specific questions or topics.

Let’s look at three of the most commonly used mixed methods research designs.

Convergent parallel design

The convergent parallel design is one of the most straightforward mixed methods approaches.

In this design, quantitative and qualitative data are collected simultaneously but analyzed separately. The key is to give both sets of data equal priority, bringing them together during the interpretation phase, before then comparing and contrasting the results to see where they converge or diverge.

This design is particularly useful when you want to verify findings and gain a more comprehensive understanding of the topic. For example, you might use quantitative surveys to gather broad data on customer satisfaction while conducting interviews to explore the reasons behind the survey responses.

Explanatory sequential design

In the explanatory sequential design , research begins with quantitative data collection and analysis, followed by qualitative methods to explain or build on the quantitative results. This design is especially useful when you have clear quantitative findings that require further exploration to understand their underlying reasons.

For instance, imagine you conduct a survey that reveals a significant drop in employee engagement. While the quantitative data shows the trend, it doesn’t explain why this drop is happening. So, you follow up with in-depth employee interviews to explore the factors contributing to the decline.

Exploratory sequential design

The exploratory sequential design flips the order of the explanatory sequential design. Here, you start with qualitative data collection to explore a topic, and then follow up with quantitative methods to test and validate those initial insights.

This design is ideal when you’re entering a research area where little is known, or when you need to develop a framework or theory that can later be tested.

For example, if you’re launching a new product and want to understand potential customer needs and preferences, you might begin with focus groups or interviews – helping you identify key themes and variables. Once you have a clearer picture, you can design a quantitative survey to test these themes on a larger scale to ensure that your findings are broadly applicable to your audience.

Mixed methods research in action

Let’s take a look at how mixed methods research can be applied in the real world.

A fintech company is working to design a user-friendly mobile banking app that enhances its customer experience – for both tech-savvy users and people who may be less familiar with digital banking – while meeting stringent regulatory requirements.

To achieve this – and create a mobile banking app that’s both user-centered and well-grounded in data – the company employs a mixed method approach, leveraging quantitative and qualitative methods across several phases.

Phase 1: Foundational research

To start, the company conducts in-depth interviews with potential users to uncover their needs, challenges and preferences, and shape the initial direction of the app. Alongside this, a quantitative market analysis is performed to understand broader trends and competitor offerings.

The combination of these methods gives the company a well-rounded view of both user expectations and its market landscape. The company uses this information to come up with a detailed concept for their digital banking app.

Phase 2: Concept testing

Next, the company tests its initial app concepts.

It deploys focus groups to gather qualitative feedback, helping the team refine ideas based on user reactions. To validate these insights, a concept testing survey is conducted with a larger user base, helping the team understand which concepts are most appealing.

Phase 3: Prototyping and usability testing

As the app design progresses to the prototyping stage, the fintech company uses unmoderated user testing to see how users interact with the prototype. This feedback reveals usability issues and helps refine the design.

At the same time, usability metrics – such as task success rates and time on task – are tracked to quantitatively measure the app’s performance.

This process is iterative, they revise the prototypes based on feedback and test again to make sure the changes improve users’ experiences with the app.

Phase 4: Launch and initial monitoring

At the app’s launch, the company closely monitors user interactions through usage analytics , providing quantitative insights into how the app is being used – and where that usage leads to frustration like rage clicks . To complement this data, user feedback sessions gather qualitative insights directly from users to better understand the cause of the frustration.

This combination allows the company to quickly identify and address any issues, ensuring a smooth user experience from the start.

Phase 5: Continuous improvement

Post launch, the company moves into the continuous improvement phase to ensure that the app evolves with user needs and stays competitive in the market.

Digital customer satisfaction and NPS survey s are regularly used to collect quantitative data on user satisfaction and loyalty. Alongside these efforts, the company engages with users in online communities to gather qualitative feedback on their experiences and suggestions.

How to conduct effective mixed methods research

Great mixed methods research requires careful planning and execution at every stage.

By following a structured approach, you can ensure that your research delivers comprehensive, actionable insights.

Here are the three main areas to get right.

Defining research questions

The first step in any research project is defining clear and focused research questions. In mixed methods research, it’s vital that you work around research questions that can be answered using both qualitative and quantitative methods.

Start by considering what you want to learn and how each method can contribute to answering your questions. For instance, you might use quantitative methods to measure the extent of a problem and qualitative methods to explore the underlying causes.

Choosing the right design

Once your research questions are defined, the next step is choosing the right mixed methods design.

Whether you opt for a convergent parallel design , an explanatory sequential design or an exploratory sequential design really depends on your specific research goals.

Here, think about how you want to integrate the qualitative and quantitative data. Do you want to collect them simultaneously or sequentially? Which method will take precedence?

Integrating and interpreting data

The true power of mixed methods research lies in the integration and interpretation of your data.

After you’ve collected your data, the challenge is to bring it all together in a meaningful way. Look for points of convergence where the data supports the same conclusions, as well as areas where the results diverge.

The use of a single platform can make a big difference during this phase – especially one that offers data integration and analysis. By having all your data in one place, you can compare and contrast findings with far greater ease – leading to richer interpretations and more robust conclusions a lot faster.

Discover how Qualtrics simplifies mixed methods research

Today’s leading organizations are leveraging mixed methods research to gain deeper insights, validate hypotheses and seamlessly incorporate findings into every phase of business building – from creating productive, cohesive teams, to developing game-changing products.

Download the free Qualtrics ebook today to learn how you can enhance and combine your research capabilities to drive meaningful change across your organization.

This comprehensive guide will show you how to future-proof your surveys, leverage AI-powered tools to save time and money and make qualitative analysis more efficient than ever before.

Free eBook: Breaking down silos: Combining qualitative and quantitative research

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