Instructions for Combining Quantitative and Qualitative Research Approaches

The embedded research design is a valuable approach in mixed-methods research, offering several advantages that make it a popular choice among researchers, particularly in fields such as health and social sciences.

This design, which has been significantly developed by researchers such as Greene, Caracelli, and Graham, Creswell and Plano Clark, and more recently, PD Dr. Ivar Krumpal from the University of Leipzig, serves to supplement data, create a collaborative environment, and provide funding opportunities for research teams.

The embedded design is often employed to answer secondary research questions or to provide deeper insights into the primary study. It complements the primary focus of the study, making it suitable for researchers with limited resources or less experience with the secondary method.

One of the key features of the embedded design is the embedded correlational model. This model, which operates under the philosophical assumptions of the primary approach, whether post-positivist or constructivist, seeks to explain relationships between variables.

Data collection in this model follows a predominantly quantitative structure, with qualitative data playing a supportive role. Researchers typically begin with quantitative methods such as surveys or large-scale questionnaires to identify correlations between key variables. In the embedded correlational model, qualitative data is embedded within a primarily quantitative study plan.

The primary goal of the embedded correlational model is to understand the factors influencing the associations found in traditional correlational research. The impacts of this approach should be discussed, including how the embedded design contributes to a deeper understanding of the research problem.

Researchers should consider how the second type of data will complement the primary data, ensuring that the secondary data provides additional insights that enhance or clarify the main findings. Data collection and analysis should align with the chosen structure of the study, with primary data analysis typically occurring first, followed by secondary data analysis.

The interpretation of results should go beyond merely presenting two sets of results and focus on how the embedded data strengthens or challenges the primary results. The embedded design acknowledges that the philosophical underpinnings of qualitative (constructivist) and quantitative (post-positivist) approaches are not inherently incompatible but can be strategically combined.

It's essential to note that the embedded design has two main variants: the experimental model and the correlational model, which differ in their purpose and data integration strategies. The embedded experimental model aims to understand intervention processes or outcomes, while the correlational model seeks to explain relationships between variables.

The embedded research design in mixed-methods research integrates qualitative and quantitative data within a primary research framework. The quantitative method is dominant, while the qualitative component supports and enriches the quantitative data. This model is particularly useful when dealing with complex social, behavioral, or psychological phenomena where numerical trends alone may not fully capture the nuances of human interaction.

In conclusion, the embedded research design offers a powerful tool for researchers seeking to gain a deeper understanding of their research subjects. By strategically combining qualitative and quantitative approaches, this design provides a more comprehensive view of complex phenomena, making it a valuable addition to any researcher's toolkit.