Analyzing Mixed Methods Data
The following module will discuss strategies for analyzing the data collected in mixed methods research.
- Explain the difference between concurrent and sequential data analysis.
- Describe the basic data analysis procedures for both the qualitative and quantitative data collected.
- Describe strategies for integrating the qualitative and quantitative data in order to interpret the results.
Mixed methods data analysis will involve analyzing the data from both the qualitative and quantitative approaches used in the study. The strategy for data analysis and the timing of the analysis may be driven by the overall rationale or purpose for using mixed methods such as triangulation, complementarity, development, initiation, and expansion. These rationales were explained in more detail in a previous module. The rationale behind the mixed methods approach typically drives the design of the research study and therefore, determines at what point in the project data is collected and analyzed. In general, the qualitative and quantitative data will either be analyzed concurrently or sequentially. For example, if the purpose of using mixed methods is triangulation, then data will likely be collected and analyzed concurrently. If the purpose is for development, the study will be designed to occur in phases and data from the first phase will be collected, analyzed and used to develop the second phase.
Regardless of when the data is collected and analyzed within the study, there are general procedures for data analysis for both qualitative and quantitative data. The chart below outlines the basic processes. The exact methods for qualitative and quantitative data analysis were discussed in detail in those Research Ready units.
Once the data from the qualitative and quantitative portions of the study have been analyzed, the data will need to be integrated in an appropriate way depending on the purpose of the study. The integration of the data will maximize the strengths of each approach, while minimizing the weaknesses. Effective integration of the data is the key to utilizing the mixed methods approach to draw more comprehensive conclusions from a project. Three common methods for data integration are discussed below:
- Data Consolidation or Merging – This can be achieved by jointly reviewed both types of data and consolidating them through the use of numeric codes or narrative. It typically requires transforming one dataset so that it can be compared to the other data set. For example, perhaps numeric codes are assigned to the narrative data collected in a qualitative study so that the results can be compare to the quantitative results.
- Connecting Data – When connecting data, one set of data is analyzed and the results are used to guide the subsequent data collection, thus make a connection between data sets, but not directly comparing results. This is used in two-phase projects where data is collected and analyzed sequentially.
- Embedding the Data – One set of data is considered to the primary source and the second set of data is embedded in the first one. For example, the primary data in a project may relate to quantitative data in a drug therapy trial. However, within the project, a smaller set of qualitative narrative responses may be collected from participants and used to supplement the results collected from the trial.
The following Slideshare presentation, Mixed Methods Data, offers a review of mixed methods research design and data collection processes. It then discusses the types of data analysis that are appropriate for different types of research design. The next module in this series will discuss how to utilize software packages to assist in the data analysis process.
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