Zickmund SL (VA Pittsburgh, CHERP), Obrosky DS
(VA Pittsburgh, CHERP)
Health Services Research and Development within the Veterans Administration has been at the forefront of supporting studies using mixed methods data. Such a rich approach to data is especially important in implementation studies where investigators must be sensitive to idiosyncratic barriers that may reduce the effectiveness of an intervention. What is less clear is how to integrate qualitative and quantitative components of a study at the level of the analytical plan. The object of this presentation is to explore ways to (1) convert qualitative text into numerical data, and (2) to use these data to strengthen and enrich the quantitative analysis section of grants and articles.
Before attempting to convert qualitative data to numerical form, transparent criteria should be developed for the qualitative data analysis. These include code development and assignment, as well as using coding strategies with sufficient clarity to enable the creation of binary or ordinal categories. After this process, qualitative data may be analyzed following preset criteria. Examples of how to incorporate qualitative data include: using quantitative outliers for close textual qualitative analysis, use of the qualitative data to complement quantitative measures (such as surveys) collected in the same population, and using statistical correlations of qualitative codes with a key outcome variable in order to determine which codes to choose for the qualitative analysis.
The above methodological issues will be demonstrated using current mixed methods data from the Merit Review Patient/Provider Attitude Toward Hepatitis Study (PATHS). The results will include a discussion of the development of a qualitative codebook to facilitate the creation of quantitative categories, the relationship between parallel qualitative codes and survey data, and an initial approach to the use of qualitative data within statistical models.
Continued methodological work will help to advance the integration of qualitative and quantitative data at the analytical level.
The use of mixed methods data require transparent and complex analytic approaches to ensure that the data are used most effectively in the design and assessment of implementation studies.