LI Course
CE Hours: 3 contact hours

Statement of Purpose and Institute Overview: The purpose of this institute is to demonstrate how to integrate patient perspectives and policy-relevant quantitative data in health access research. Although there is broad appeal for combining patient perspectives and the traditional structural barriers in understanding disparities in health care access, methods for triangulating the two explanatory approaches have not been adequately established. Combining qualitative and quantitative data can lead to a better understanding of barriers to health care access and adherence. Traditional approaches of analyzing patient perspectives qualitatively without linking them to policy-relevant outcomes fail to establish the role of socio-cultural factors in health policy interventions; while researcher-derived quantitative measures may not be sensitive enough. Course content: A study entitled "Cultural concepts of cancer mammography access and adherence" is used to demonstrate an approach for combining patient-derived qualitative data and policy-relevant quantitative data. The course is administered in three components. The first component focuses on methods that were used to collect population-based dataset and findings of a prior analysis. The Second component provides a step-by-step demonstration of how to combine qualitative and quantitative data. The Framework (Pope, Ziebland, and Mays, 2000) and explanatory account approaches (Stern & Kirmayer, 2004) are used to analyze the qualitative data and develop quantitative variables from qualitative data; while factor analysis and correlation of qualitatively-derived variables with well established constructs is used to validate the qualitative variables. Triangulation is achieved by merging the validated qualitatively-derived variables with quantitative variables. Frame-analysis is the key to identifying empirically active qualitative variables: Bivariate associations between qualitative derived measures reveal a consistent pattern of associations and an empirically active frame. Correlation analysis of qualitative variables with external constructs reveals consistent convergent/divergent patterns of associations. In multivariate adjustments, qualitative data-derived measures remain important predictors of mammography uptake and adherence while health insurance remains an important predictor of mammography uptake, not adherence. The third component will address how to develop more policy-relevant dependent variables.

Session Objectives: Upon course completing participants will: 1)Appreciate the value in combining qualitative and quantitative measures 2)Understand how to develop valid, empirically active quantitative variables from qualitative data. 3)Describe how to integrate qualitative and quantitative measures 4)Understand how to develop policy-relevant dependent variables

CE Credits: CME, Health Education (CHES), Nursing