How to Integrate Quantitative & Qualitative Data? | Mixed Methods

Integration serves as the central element of mixed methods research, distinguishing it from other methodologies that do not emphasize the combination of different data sources. Integration occurs at the stage where qualitative and quantitative data interact within the research process. The following article elaborates on how qualitative and quantitative data integrative analysis align across designs.

Introduction

The current thinking when it comes to analyzing data in mixed methods research rests in integration, which is the point where qualitative and quantitative data cross.

Integration is the phase in a mixed methods research process where the quantitative and qualitative data intersect. This is sometimes called the point of interface (Morse & Niehaus, 2009). Integration can involve the dissolving of one kind of data into the other, or the different datasets can remain separated while their findings become interrelated. In a good mixed methods research study, the integration part is explicit and the researcher goes through and clearly shows how the data was integrated. This can be through a discussion, table, or graph such as a joint display.

In a convergent design, integration comes when the qualitative and quantitative data are combined or merged. In a sequential design, the integration comes between the qualitative and quantitative phase, or between the quantitative and qualitative data.

However, integration is often poorly described in the literature and is sometimes absent in studies, as researchers may equate conducting mixed methods research with merely collecting and analyzing both quantitative and qualitative data (Bryman, 2006). This approach does not capture the full potential of a mixed methods approach, which lies in the emergence of additional insights that extend beyond the separate results of qualitative and quantitative methods.

Given the centrality of integration in conducting mixed methods research, it is essential to thoroughly understand and prioritize it within mixed methods data analysis. Integration varies according to the mixed methods design, and recent literature offers better frameworks for conceptualizing it.

Essential elements for integration and interpretation

Creswell’s (2016) framework outlines four essential considerations for researchers to address when planning and conducting integrative analyses and interpretation: the purpose of integration, the analytical procedures used for integration, the presentation of integrated findings, and the interpretation of those findings.

What is the purpose?

The purpose of integration clarifies why researchers choose to combine qualitative and quantitative data in a study. The rationale behind integration depends on what the researcher aims to achieve within a specific mixed methods approach. This purpose can be explicitly stated in an integration intent statement, which may be included in the introduction or methodology section of a dissertation proposal, grant application, or journal article on conducting mixed methods research.

What are the analytical procedures involved?

The analytical procedures for integration outline the steps taken to fulfill the integration’s purpose. These procedures describe how the researcher systematically links and analyzes qualitative and quantitative methods and are detailed in the methods section of a proposal or report. The specific procedures used will depend on the study’s mixed methods approach and design.

Presentation of integrated findings

The presentation of integration results pertains to how the outcomes of the integration are conveyed in a study. Typically included in the results or discussion section, integrated findings can be presented through narrative descriptions or visual formats. A common visual tool is the joint display, which merges qualitative and quantitative research results in a structured way, adapting to the study’s design.

Interpretation of findings

The interpretation of integrated findings involves drawing conclusions from the combined data and visual representations to address the mixed methods research question. These interpretations provide deeper insight by linking the integrated results to the study’s overall purpose and integration intent, and they are discussed in the final analysis of the research.

Convergent design and integration

In a convergent design, the researcher collects quantitative and qualitative data simultaneously, analyzes them separately, and then integrates both datasets to form a comprehensive interpretation.

The goal of integration in a convergent design is to generate findings that enhance understanding, provide a more complete perspective, and ensure validation through data confirmation. To achieve this, researchers merge the results to address the mixed methods research question by either comparing the two datasets or transforming one dataset for combined analysis with the other dataset.

For a convergent design, the analysis should directly respond to mixed methods questions, such as: To what extent do the quantitative and qualitative data align or differ? Do the transformed qualitative findings meaningfully contribute to the quantitative results? This merging process is also referred to as simultaneous integration (Morse & Niehaus, 2009).

When conducting mixed methods data analysis within a convergent design, researchers must determine the most suitable integration procedures, decide on the best way to represent the merged findings, and establish a clear approach for interpreting the combined results.

Primary data analysis integration in a convergent design

A common strategy in convergent design is to compare the results from the quantitative and qualitative data after analyzing them separately. This process involves several key steps:

1. Conduct separate analyses of the quantitative and qualitative data to generate results for each dataset.
2. Identify common themes or concepts across both sets of findings. This step requires aligning data collection on similar topics to facilitate meaningful comparisons.
3. Create joint displays, such as tables or graphs, to visually organize and align the findings from both datasets, making comparisons more straightforward.
4. Analyze the joint displays by examining how the findings correspond, diverge, or complement one another.
5. If discrepancies arise, apply additional strategies to explore and understand conflicting results.
6. Interpret the integrated findings by assessing how the confirming, contradictory, or expanded evidence contributes to a deeper understanding of the research problem.

By following this approach, researchers can systematically integrate quantitative and qualitative research findings to provide a more nuanced and comprehensive interpretation of the data.

Data transformation in convergent designs

Data transformation in mixed methods research refers to the process of converting one type of data (qualitative or quantitative) into the other to facilitate integration and comparison. This approach allows researchers to analyze qualitative and quantitative data in a unified way, making it easier to draw meaningful conclusions.

Data transformation has been widely discussed in mixed methods research, with notable contributions from Caracelli and Greene (1993), Onwuegbuzie and Teddlie (2003), and Sandelowski, Voils, and Knafl (2009).

The key general steps in this approach include:

1. Conduct separate analyses of the quantitative and qualitative data to generate initial findings.
2. Convert one dataset into the format of the other (e.g., transforming qualitative themes into numerical variables or coding quantitative results into qualitative categories).
3. Integrate the transformed data into the corresponding dataset, either by conducting additional quantitative analyses using the transformed qualitative data or performing further qualitative analysis using the converted quantitative data.
4. Create joint displays such as tables or graphs that visually represent the merged results.
5. Interpret how the transformed data contributes to a deeper understanding of the research question.

More commonly, qualitative data is transformed into quantitative data, reducing themes or codes into numerical formats such as dichotomous variables (e.g., the presence or absence of a theme scored as 1 or 0). Onwuegbuzie and Teddlie (2003) provided detailed procedures for quantifying qualitative data, including:

• Converting theme frequency into percentages within a dataset.
• Calculating the proportion of total themes associated with a phenomenon.
• Measuring the percentage of participants endorsing multiple themes.
• Counting instances of behaviors, statements, or observations over time.

An example of data transformation in convergent design is seen in Daley and Onwuegbuzie’s (2010) study on violence attribution among male juvenile delinquents. They correlated closed-ended responses with open-ended themes by dichotomizing each qualitative theme (1 = present, 0 = absent) and comparing these scores against the quantitative dataset. Similarly, Sandelowski (2003) transformed qualitative interview data into a statistical comparison of medical decisions during pregnancy.

Fewer studies demonstrate the reverse—transforming quantitative data into qualitative data—but examples exist. Punch (1998) described converting factor analysis results into themes for comparison with existing qualitative categories. Teno, Stevens, Spernak, and Lynn (1998) transformed quantitative medical records and survival predictions into narrative summaries in their study on the use of advance directives.

Interpretation of integrated results in convergent designs

After merging data through a narrative discussion or joint display, researchers must interpret how the combined findings address the mixed methods research question. This interpretation depends on the chosen procedures. In data transformation, researchers assess significant relationships, meaning, and limitations.

When comparing datasets, researchers look for consistencies, conflicts, and contradictions. Identifying whether findings align or diverge helps researchers assess their study’s validity.

If discrepancies arise, researchers have several options: citing methodological limitations, collecting additional data, or using inconsistencies to guide future research. A cost-effective approach is to reexamine existing data. For example, a study followed this method in the Harlem Mammogram Study, where initial analyses failed to explain delays in seeking care. By revisiting qualitative themes, researchers found quantitative data that supported participants' statements, leading to deeper insights and further research.

Thus, in a convergent design, interpretation involves identifying congruences and discrepancies and using them to refine understanding of the research problem.

Integration in explanatory sequential design

The explanatory sequential design in mixed methods research involves multiple stages of data collection and integration. The process begins with collecting and analyzing quantitative data, which guides the subsequent qualitative phase. The qualitative data is then used to provide deeper insights into the quantitative results.

The integration of data in this design connects the quantitative and qualitative phases to ensure the qualitative findings explain specific results from the quantitative phase. This involves identifying unexpected, perplexing, or extreme results in the quantitative data and using qualitative data to explore them further. The strong connection between the phases, such as selecting qualitative participants based on quantitative findings, ensures meaningful explanations.

The explanatory sequential design follows three main phases of data integration:

1. Phase 1: Analyzing quantitative data – Identifying statistical results that require further explanation.
2. Phase 2: Selecting participants for the qualitative phase – Purposefully choosing individuals from the quantitative phase to explain the findings.
3. Phase 3: Analyzing qualitative data – Interpreting how qualitative insights clarify quantitative results.

Key steps in the integration process include:

1. Analyzing quantitative data to identify patterns or anomalies.
2. Purposefully sampling participants for the qualitative phase.
3. Designing qualitative data collection methods to address specific questions.
4. Developing joint displays, such as tables or graphs, to illustrate how qualitative findings enhance quantitative results.
5. Interpreting the additional value qualitative data provides to understanding the quantitative results.

Sampling strategies for integration

Researchers can select participants for the qualitative phase in several ways:

Typical cases – Selecting participants who represent the average within different groups to understand group variations.
Extreme cases – Identifying outliers to explore why they differ from the norm.
Group comparisons – Choosing participants from statistically different groups to examine the reasons for differences.
Significant predictors – Sampling individuals with varied scores on key predictors to understand different perspectives.

Representation of integration

To effectively represent integration, researchers use joint displays—visualizations such as tables or graphs—to demonstrate the connection between quantitative and qualitative data. This helps clarify how qualitative insights enhance understanding of the quantitative results.

Interpretation of integrated results

The interpretation of results in an explanatory sequential design emphasizes how qualitative data deepens the understanding of quantitative findings. Instead of merely comparing the two datasets, the goal is to refine the interpretation of statistical results using qualitative insights. This approach aligns with the intent of explanatory sequential design, which seeks to expand upon specific aspects of quantitative findings rather than conduct direct comparisons.

This design ensures that the qualitative phase is directly informed by and connected to the quantitative results, making it a structured and purposeful approach to mixed methods research.

Exploratory sequential design integration

In an exploratory sequential design, the integration of mixed methods data occurs at multiple points. The process begins with a qualitative exploration, followed by the development of a quantitative feature (such as a survey, intervention, or variable), which is then tested on a larger sample.

Intent of integration

The goal of integration in an exploratory sequential design is to ensure that the quantitative phase is informed by qualitative insights, making it contextually and culturally appropriate. The integration process involves using qualitative findings to shape the quantitative feature, ensuring that personal experiences and cultural contexts are embedded in its design. This approach allows researchers to answer questions such as:

How can qualitative insights shape the development of an instrument tailored to participants’ needs?
How can qualitative findings enhance recruitment strategies or intervention designs?

Data analysis integration procedures

The integration process consists of three main phases:

Analyzing qualitative data – Identifying themes and codes that capture participants’ personal experiences or contribute to theoretical understanding.
Building a quantitative feature – Designing a survey, intervention, or variable informed by qualitative insights.
Testing and analyzing quantitative data – Evaluating the effectiveness and cultural relevance of the developed quantitative feature.

Key steps include:

1. Analyzing qualitative data to extract relevant themes and typologies.
2. Determining the necessary quantitative feature (e.g., intervention, survey, or variable).
3. Mapping qualitative findings to quantitative components (e.g., using quotes to design survey items).
4. Creating a joint display to illustrate how qualitative insights inform the quantitative feature.
5. Pilot testing and refining the quantitative feature.
6. Conducting a large-scale test of the quantitative feature.
7. Interpreting the results to assess cultural sensitivity and relevance to the target population.
8. Building connections between qualitative and quantitative phases

A critical step in exploratory sequential design is ensuring a strong connection between the qualitative findings and the development of the quantitative feature. This may involve:

Extracting key themes and representative quotes to shape survey questions or intervention components.
Identifying patterns in qualitative responses that inform the structure of a survey or the categories of an instrument.
Developing typologies based on qualitative insights to guide the creation of new variables.

For example, Meijer, Verloop, and Beijaard (2001) conducted a qualitative study on language teachers’ knowledge before designing a questionnaire. They used qualitative categories to structure survey items, adapting them to different teaching contexts.

Representation through joint displays

Joint displays visually represent how qualitative data informs the development and testing of quantitative features. Two main types of joint displays in exploratory sequential design include:

Connection joint display – Illustrates how qualitative findings influence the design of a quantitative feature (e.g., Peterson et al., 2013, used qualitative data to tailor intervention content).

Validation joint display – Shows how the tested quantitative feature aligns with qualitative themes and cultural contexts (e.g., Watkins et al., 2015, mapped qualitative themes onto a national survey database to create a new variable).

Interpretation of integration results

The final step involves interpreting how the quantitative feature, shaped by qualitative insights, enhances understanding of the studied phenomenon. Researchers assess whether the quantitative tool captures cultural and contextual nuances effectively.

For example, Watkins et al. (2015) analyzed how qualitatively derived variables performed in a quantitative study on African American churchgoing men’s mental health. Their joint display demonstrated that themes identified in the qualitative phase—such as help, communication, and closeness—formed a valid latent variable, reinforcing the cultural relevance of their findings.

In other studies, researchers might discuss how the qualitative phase shaped the design of a culturally sensitive instrument, intervention activities, or survey instrument selection. The discussion should explicitly highlight how qualitative insights strengthened the quantitative component and improved the study's overall explanatory power.

Conclusion

Integration is the defining feature of mixed methods research, allowing qualitative and quantitative data to interact in meaningful ways. By strategically merging, connecting, or embedding these data types, researchers can achieve a deeper and more comprehensive understanding of their research questions. The choice of integration method depends on the study’s design—whether convergent, explanatory sequential, or exploratory sequential—but in all cases, the goal remains the same: to generate insights that go beyond what either method could achieve alone.

A well-integrated mixed methods study explicitly outlines how qualitative and quantitative data are linked, using clear analytical procedures and structured presentations such as joint displays. The interpretation of integrated findings further strengthens the study’s conclusions by assessing areas of alignment, divergence, and expansion, ensuring that each phase contributes meaningfully to the overall research objective.

Despite its importance, integration is often underdeveloped in mixed methods research. Researchers must be intentional in their approach, selecting appropriate integration strategies and ensuring transparency in reporting. By doing so, they enhance the rigor of their studies and maximize the value of mixed methods research for addressing complex questions in various fields.

References

1. Bryman, A. (2006). Integrating quantitative and qualitative research: How is it done? Qualitative Research, 6(1), 97–113.
2. Daley, C. E., & Onwuegbuzie, A. J. (2004). Attributions toward violence of male juvenile delinquents: A concurrent mixed-methodological analysis. The Journal of Social Psychology, 144(6), 549–570.
3. Meijer, P. C., Verloop, N., & Beijaard, D. (2001). Similarities and differences in teachers' practical knowledge about teaching reading comprehension. The Journal of Educational Research, 94(3), 171–184.
4. Morse, J. M., & Niehaus, L. (2009). Mixed method design: Principles and procedures. Left Coast Press.
5. Onwuegbuzie, A. J., & Teddlie, C. (2003). A framework for analyzing data in mixed methods research. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 351–383). Sage Publications.
6. Punch, K. F. (1998). Introduction to social research: Quantitative and qualitative approaches. Sage Publications.
7. Sandelowski, M. (2003). Tables or tableaux? The challenges of writing and reading mixed methods studies. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods in social and behavioral research (pp. 321–350). Sage Publications.
8. Teno, J. M., Stevens, M., Spernak, S., & Lynn, J. (1998). Role of written advance directives in decision making: Insights from qualitative and quantitative data. Journal of General Internal Medicine, 13(7), 439–446.
9. Watkins, D. C., Walker, R. L., & Griffith, D. M. (2015). A meta-study of Black male mental health and well-being. Journal of Black Psychology, 41(4), 305–327