Explanatory Sequential Design in Mixed Method Research: A Comprehensive Guide

What is Explanatory Sequential Design Mixed Method Research?

Understanding Mixed Method Research

Mixed method research is a type of research that combines quantitative and qualitative approaches to gather and analyze data. The explanatory sequential design is a popular mixed methods design that integrates both quantitative and qualitative data collection and analysis. This research method involves collecting quantitative data first, followed by qualitative data to explain or elaborate on the quantitative results. The explanatory sequential design is particularly useful when researchers need to explore quantitative findings in more depth. By using this approach, researchers can gain a more comprehensive understanding of complex phenomena and address research questions that cannot be fully answered by either quantitative or qualitative methods alone.

Key Features of Explanatory Sequential Design

The explanatory sequential design is characterized by several key features that distinguish it from other mixed methods approaches. In this type of mixed methods research, the quantitative phase precedes the qualitative phase, with the latter used to explain the former. The explanatory sequential design consists of two distinct phases: first, researchers collect and analyze quantitative data, and then they use qualitative data collection and analysis to delve deeper into the quantitative results. This sequence allows researchers to use qualitative findings to explain and interpret quantitative results in more detail. The choice of a mixed methods design like the explanatory sequential approach is often driven by the research question and study aims. Researchers using the explanatory sequential design may employ various data collection methods, such as surveys for quantitative data and interviews or focus groups for qualitative data. The integration of quantitative and qualitative data occurs primarily during the interpretation phase of the research process.

Difference Between Explanatory and Exploratory Sequential

• While both explanatory sequential design and exploratory sequential design are types of mixed methods research, they differ in their approach and sequence. The explanatory sequential design begins with quantitative data collection and analysis, followed by qualitative research to explain the quantitative results.
• In contrast, the exploratory sequential mixed methods design starts with qualitative data collection and analysis, followed by a quantitative phase to test or generalize the initial qualitative findings. 
• The choice between explanatory and exploratory sequential designs depends on the research objectives and the nature of the research question. Explanatory sequential design is used when researchers want to explain quantitative results in more depth, while exploratory sequential design is employed when researchers need to explore a phenomenon qualitatively before developing and testing quantitative measures.
• Both designs offer unique advantages in integrating quantitative and qualitative research methods, but they serve different purposes in the research process.

How to Design an Explanatory Sequential Mixed Method Study?

Steps in Research Design 

Designing an explanatory sequential mixed method study involves several key steps:

• First, researchers must formulate a clear research question that necessitates both quantitative and qualitative approaches. The research team then develops a plan for the explanatory sequential design, outlining how quantitative and qualitative data will be collected and analyzed sequentially.
• In the initial quantitative phase, researchers design and conduct a quantitative study. This typically involves developing survey instruments, determining sampling methods, and planning for quantitative data collection. Once quantitative data are collected, researchers perform a thorough analysis of quantitative results.
• Based on the quantitative findings, researchers then design the qualitative phase. This involves developing interview guides or focus group protocols that address specific aspects of the quantitative results that require further explanation. The qualitative sample is often selected using purposive sampling, targeting participants who can provide insights into the quantitative findings.
• After qualitative data collection and analysis, researchers integrate the quantitative and qualitative results. This integration is a crucial step in the explanatory sequential design, as it involves using qualitative findings to explain and interpret quantitative results.
• Throughout the research process, it’s essential to consider how the explanatory sequential design aligns with the study aims and research objectives. Researchers should also plan for potential challenges associated with this type of mixed methods research, such as maintaining consistency between the two phases and ensuring that the qualitative phase adequately addresses the quantitative findings.
• Finally, researchers should consider how they will report the results of their explanatory sequential study, ensuring that both quantitative and qualitative findings are presented cohesively and that the integration of the two methods is clearly explained.

Choosing Between Qualitative and Quantitative Data

In an explanatory sequential design, the choice between qualitative and quantitative data is predetermined by the nature of the design itself. However, researchers must still make important decisions about the types of data to collect in each phase. For the quantitative phase, researchers might choose between various types of quantitative data, such as survey responses, experimental results, or secondary data from existing databases. The choice depends on the research question and the variables being studied.

For the qualitative phase, researchers typically use methods such as qualitative interviews, focus groups, or open-ended surveys to collect data that can explain the quantitative results. The choice of qualitative data collection method often depends on the nature of the quantitative findings and what aspects need further explanation. Researchers using the explanatory sequential design must ensure that the qualitative data collection is designed to specifically address and elaborate on the quantitative results, maintaining the logical flow between the two phases of the study.

Factors Influencing Study Design

Several factors influence the design of an explanatory sequential mixed method study:

• The research question is paramount, as it guides the choice of a mixed methods design and determines the focus of both the quantitative and qualitative phases. The research objectives also play a crucial role, influencing the specific methods used in each phase of the explanatory sequential design.
• Practical considerations, such as time, resources, and access to participants, can impact the study design. For instance, the research team may need to consider the feasibility of conducting both quantitative and qualitative data collection within the available timeframe. The expertise of the research team in both quantitative and qualitative methods is another important factor.
• The nature of the phenomenon being studied can also influence the design. Some research topics may lend themselves more readily to certain types of quantitative or qualitative data collection.
• Finally, ethical considerations and the potential impact on participants should be taken into account when designing each phase of the explanatory sequential study.

What are the Advantages of Explanatory Sequential Mixed Method Research?

Benefits of Combining Qualitative and Quantitative Methods

The explanatory sequential design offers several benefits by combining qualitative and quantitative methods.

• This approach allows researchers to leverage the strengths of both methodologies, resulting in a more comprehensive understanding of the research problem. The quantitative phase provides a broad, generalizable picture of the phenomenon under study, while the qualitative phase offers depth and context to these findings.
• One key advantage of the explanatory sequential design is its ability to address complex research questions that cannot be fully answered by either qualitative or quantitative methods alone. By using qualitative data to explain quantitative results, researchers can uncover the underlying reasons, attitudes, and contexts that shape the quantitative outcomes.
• This mixed methods design also enhances the validity and reliability of research findings. The quantitative phase allows for statistical analysis and generalization, while the qualitative phase provides rich, detailed data that can corroborate or challenge the quantitative results. This integration of methods can lead to more nuanced and trustworthy conclusions.
• Furthermore, the explanatory sequential design can be particularly useful in applied research settings, where understanding both the “what” (quantitative) and the “why” (qualitative) of a phenomenon is crucial for developing effective interventions or policies. This approach allows researchers to not only identify trends and patterns but also to explain the mechanisms behind them.

Enhancing Research Validity and Reliability

The explanatory sequential design significantly enhances research validity and reliability through its systematic integration of quantitative and qualitative methods.

• By using qualitative findings to explain quantitative results, researchers can increase the internal validity of their studies. This approach allows for a more comprehensive examination of the research question, reducing the likelihood of overlooking important factors or misinterpreting quantitative data.
• The sequential nature of this design also contributes to its reliability. The quantitative phase provides a foundation of statistically robust findings, which are then elaborated upon and contextualized by the qualitative phase. This process allows researchers to cross-validate findings from both phases, increasing the overall reliability of the research.
• Moreover, the explanatory sequential design addresses potential weaknesses of single-method approaches. For instance, it can help overcome the limitations of quantitative research in explaining complex social phenomena by providing rich, contextual data through the qualitative phase. This complementary use of methods strengthens the overall research design and enhances the credibility of the findings.

Applications in Health Research

The explanatory sequential design has found widespread application in health research, where understanding both the prevalence of health issues and the underlying factors is crucial. This mixed methods approach is particularly useful in studies aiming to explain health behaviors, patient experiences, or the effectiveness of health interventions.

For example, an explanatory sequential study in health research might first use a quantitative survey to assess the prevalence of a specific health behavior. The subsequent qualitative phase could then involve interviews with selected participants to explore the reasons behind the observed behavior patterns. This approach allows researchers to not only identify health trends but also to understand the complex factors influencing these trends.

The explanatory sequential design is also valuable in health policy research, where quantitative data on health outcomes can be explained through qualitative exploration of stakeholder perspectives. This mixed methods approach provides a comprehensive view of health issues, informing evidence-based policy decisions and the development of effective health interventions.

What Challenges are Associated with Explanatory Sequential Mixed Methods?

Common Issues in Data Collection and Analysis

While the explanatory sequential design offers many benefits, it also presents several challenges in data collection and analysis:

• One common issue is ensuring coherence between the quantitative and qualitative phases. Researchers must carefully design the qualitative phase to address specific aspects of the quantitative results, which requires flexibility and adaptability in the research process.
• Another challenge lies in the analysis of qualitative data in relation to quantitative findings. Researchers using the explanatory sequential design must develop strategies to integrate these two types of data effectively. This can be particularly challenging when qualitative findings contradict or complicate quantitative results.
• Time management is also a significant issue in explanatory sequential studies. The sequential nature of the design means that the qualitative phase cannot begin until the quantitative data are collected and analyzed. This can lead to extended research timelines, which may be problematic in time-sensitive research contexts.
• Lastly, maintaining consistency in sampling between the two phases can be challenging, especially when using purposive sampling for the qualitative phase based on quantitative results.

Managing Mixed Method Studies

Managing an explanatory sequential mixed methods study requires careful planning and coordination:

• One of the primary challenges is ensuring that the research team has expertise in both quantitative and qualitative methods. This often necessitates collaboration between researchers with different methodological backgrounds.
• Another management challenge is maintaining the timeline of the study. The explanatory sequential design requires that the quantitative phase be completed before the qualitative phase can begin. This sequential nature can lead to delays if the quantitative phase takes longer than expected.
• Resource allocation is another critical aspect of managing these studies. Researchers must plan for the resources needed for both phases, including different types of data collection tools and analysis software.
• Ethical considerations also need to be managed carefully, especially when participants from the quantitative phase are recruited for the qualitative phase. Researchers must ensure that ethical approval covers both phases of the study and that participant confidentiality is maintained throughout the research process.

Addressing Research Problems

The explanatory sequential design, while effective for many research problems, may not be suitable for all types of research questions:

• One challenge is determining whether this design is the most appropriate approach for addressing the research problem at hand. Researchers must carefully consider whether the sequential nature of the design aligns with their research objectives.
• Another issue is dealing with unexpected quantitative results that may require a shift in the focus of the qualitative phase. Researchers using the explanatory sequential design must be prepared to adapt their qualitative data collection plans based on the quantitative findings.
• Additionally, researchers may face challenges in addressing research problems that require immediate integration of quantitative and qualitative data. The sequential nature of this design means that full integration often occurs only at the end of the study, which may not be suitable for research problems requiring ongoing integration of different types of data.
• Lastly, researchers must be prepared to address discrepancies between quantitative and qualitative findings, which can complicate the process of addressing the overall research problem.

How to Analyze Data in Explanatory Sequential Mixed Method Research?

Techniques for Analyzing Qualitative Data 

In the explanatory sequential design, the analysis of qualitative data is crucial for explaining and elaborating on the quantitative results. Researchers typically employ various qualitative data analysis techniques, such as thematic analysis, content analysis, or grounded theory approaches. The choice of technique often depends on the nature of the qualitative data and the specific aspects of the quantitative results that need explanation.

One common approach is to use the quantitative findings as a framework for the initial coding of qualitative data. This allows researchers to directly link qualitative themes to specific quantitative results. However, it’s also important to remain open to new themes that emerge from the qualitative data, which may provide unexpected insights into the quantitative findings.

Researchers using the explanatory sequential design must also consider how to present qualitative findings in a way that clearly demonstrates their relationship to the quantitative results. This often involves using quotes or case examples to illustrate how qualitative data explain or contextualize specific quantitative outcomes.

Integrating Quantitative Data Analysis

In an explanatory sequential design, the analysis of quantitative data precedes and informs the qualitative phase. Quantitative data analysis typically involves statistical techniques appropriate to the research question and type of data collected. This may include descriptive statistics, inferential statistics, or more advanced statistical modeling.

A key aspect of quantitative data analysis in this design is identifying specific results that require further explanation through qualitative methods. Researchers must carefully examine their quantitative findings to determine which aspects are most relevant to the overall research question and would benefit from qualitative exploration.

The integration of quantitative data analysis with subsequent qualitative findings is a critical step in the explanatory sequential design. This integration often involves comparing and contrasting quantitative and qualitative results, using qualitative findings to provide context for quantitative outcomes, and exploring how qualitative data help explain unexpected or complex quantitative results.

Researchers must also consider how to present integrated findings in a coherent narrative that demonstrates the complementary nature of the quantitative and qualitative phases.

Using Software for Mixed Method Analysis

Software tools play a crucial role in the analysis of data in explanatory sequential mixed method research. For the quantitative phase, statistical software packages such as SPSS, SAS, or R are commonly used to analyze quantitative data. These tools allow for complex statistical analyses and the generation of visual representations of quantitative results.

For qualitative data analysis, software like NVivo, Atlas.ti, or MAXQDA can be invaluable. These programs facilitate the coding and categorization of qualitative data, making it easier to identify themes and patterns that relate to the quantitative findings.

Some software packages are designed specifically for mixed methods research and can be particularly useful in explanatory sequential studies. These tools often allow for the integration of quantitative and qualitative data within a single platform, facilitating the process of connecting qualitative themes to quantitative results.

When choosing software for an explanatory sequential study, researchers should consider the specific needs of their project, the types of data being collected, and the analytical techniques they plan to use in both the quantitative and qualitative phases.

Examples of Successful Explanatory Sequential Mixed Method Studies

Case Studies in Social Research 

Explanatory sequential design has been successfully employed in various social research studies, demonstrating its effectiveness in addressing complex social phenomena. One notable example is a study conducted by Creswell et al. on academic achievement among first-generation college students. In this study, the researchers first collected quantitative data on student performance and demographic factors. The quantitative results revealed unexpected patterns in academic achievement, which were then explored through in-depth qualitative interviews with selected students. This explanatory sequential approach allowed the researchers to not only identify trends in academic performance but also to understand the unique challenges and strategies employed by first-generation students.

Another successful application of the explanatory sequential design in social research was a study on workplace diversity initiatives. The researchers initially used a quantitative survey to assess the prevalence and perceived effectiveness of diversity programs across multiple organizations. The surprising quantitative findings, which showed discrepancies between program implementation and employee perceptions, were then investigated through qualitative interviews with HR managers and employees. This mixed methods approach provided a comprehensive understanding of the complexities involved in implementing and experiencing workplace diversity initiatives, offering valuable insights for both theory and practice in organizational diversity management.

Research Topics in Health Research

The explanatory sequential design has proven particularly valuable in health research, where understanding both the prevalence of health issues and the underlying factors is crucial. A notable example is a study on medication adherence among patients with chronic conditions. The research team first conducted a quantitative study using pharmacy refill data to measure adherence rates across different patient groups. The quantitative results revealed unexpected patterns of adherence, which were then explored through qualitative interviews with patients and healthcare providers. This explanatory sequential approach allowed researchers to identify not only the rates of medication adherence but also the complex personal, social, and systemic factors influencing patient behavior.

Another successful application of this design in health research focused on the effectiveness of a community-based health intervention. The study began with a quantitative analysis of health outcomes before and after the intervention. The quantitative results showed improvements in some health indicators but not others. These findings were then explained through qualitative focus groups and interviews with community members and health workers. The qualitative phase revealed important contextual factors that influenced the intervention’s effectiveness, providing crucial insights for improving future health

Insights from Mixed Methodologists

Leading mixed methodologists, such as Creswell and Plano Clark, have provided valuable insights into the effective use of explanatory sequential design and other mixed methods approaches. These experts emphasize that while the explanatory sequential design is powerful, it’s just one type of design within the broader spectrum of mixed methods research. They highlight the importance of understanding various mixed method research designs, including convergent parallel design, embedded design, and multiphase mixed methods, to choose the most appropriate approach for a given research question.

Creswell and Plano Clark stress that designing a mixed methods study involves careful consideration of how and when to integrate qualitative and quantitative data. In contrast to the explanatory sequential design, the convergent mixed methods design involves collecting and analyzing both types of data simultaneously. The convergent design, also known as the convergent parallel design, is used when researchers want to compare and contrast quantitative and qualitative findings within a single study.

Mixed methodologists also emphasize the importance of advanced mixed methods research techniques, such as using qualitative findings to develop quantitative instruments or employing a design in which qualitative and quantitative phases inform each other iteratively. These approaches demonstrate how mixed methods research combines quantitative and qualitative strategies to provide a more comprehensive understanding of complex phenomena.

Future Directions in Explanatory Sequential Mixed Method Research

As the field of mixed methods research continues to evolve, several key areas are emerging as important for future research using the explanatory sequential design:

• Integration of Big Data: Future studies may explore ways to incorporate big data analysis into the quantitative phase of explanatory sequential designs, potentially offering new insights that can be further explored qualitatively.
• Longitudinal Applications: Researchers may develop innovative approaches to apply the explanatory sequential design in longitudinal studies, allowing for the exploration of how phenomena change over time through both quantitative and qualitative lenses.
• Cross-Cultural Adaptations: As global research collaborations increase, future work may focus on adapting the explanatory sequential design for cross-cultural studies, ensuring that both quantitative and qualitative phases are culturally sensitive and relevant.
• Technology-Enhanced Data Collection: The use of mobile apps, wearable devices, and other technologies for data collection in both quantitative and qualitative phases may open new possibilities for explanatory sequential research.
• Ethical Considerations: Future research may delve deeper into the ethical implications of sequential designs, particularly in sensitive research areas where quantitative findings might influence the nature of subsequent qualitative inquiries.
• Mixed Methods in Action Research: Exploring the application of explanatory sequential design in action research contexts could provide valuable insights for practitioners and researchers alike.
• Enhancing Reporting Standards: Developing more comprehensive guidelines for reporting explanatory sequential studies could improve the transparency and replicability of this research approach.

These future directions highlight the ongoing evolution of mixed methods research and the potential for explanatory sequential design to address increasingly complex research questions across various disciplines.

Conclusion

The explanatory sequential design represents a powerful approach within the realm of mixed methods research. By combining the strengths of quantitative and qualitative methodologies, this design offers researchers a comprehensive tool for addressing complex research questions. The sequential nature of the design, where qualitative data are used to explain quantitative results, provides a unique opportunity to delve deeper into research findings and uncover the underlying mechanisms of observed phenomena.

As we have seen through various examples and insights from mixed methodologists, the explanatory sequential design has wide-ranging applications across social sciences, health research, and beyond. Its flexibility allows researchers to adapt the design to various contexts, from basic mixed methods studies to more advanced mixed methods research projects.

However, it’s important to recognize that the explanatory sequential design is just one type of design within the broader spectrum of mixed methods approaches. Researchers should carefully consider their research objectives and the nature of their inquiry when choosing between explanatory sequential, convergent parallel, embedded, or other mixed methods designs.

The future of explanatory sequential mixed method research looks promising, with potential advancements in data integration techniques, technological applications, and cross-disciplinary collaborations. As researchers continue to refine and expand upon this methodology, it will undoubtedly contribute to more nuanced and comprehensive understandings of complex social and health-related issues.

In conclusion, the explanatory sequential design offers a robust framework for researchers seeking to combine the breadth of quantitative inquiry with the depth of qualitative exploration. By thoughtfully applying this approach and continuing to innovate in its application, researchers can unlock new insights and drive forward our understanding of multifaceted research problems.

FAQs about Explanatory Sequential Design