This presentation provided a basic introduction to qualitative research and scholarship methods in medical education. I began with an overview of qualitative approaches, including scholarship paradigms, designs, methods, and data collection and analysis. I then reviewed the uses of qualitative approaches in medical education and outlined an expanded definition of scholarship, as articulated by Ernest Boyer and Charles Glassick, which makes the use of qualitative methods more important in medical education. I devoted considerable attention to procedures for analysis of qualitative data. I concluded with a discussion of approaches for assessing the reliability, validity, and trustworthiness of results, and resources for medical science educators to enhance their skills in use of qualitative methods.

In her introduction to this series, Dr. Carol Hodgson outlined two general research paradigms: the inductive approach of beginning with a theory and collecting data to test it (the focus of her presentation, and the second presentation by Dr. Larry Gruppen): and the inductive approach, of beginning with observations from which hypotheses and themes are developed. Qualitative approaches fit the inductive paradigm. Dr. Hodgson also outlined two general approaches to research design: confirmatory (experimental, quasi-experimental, and correlational) and exploratory. Qualitative approaches are typically exploratory. Qualitative data is data in the form of words, rather than numbers, typically obtained by the research methods of asking open-ended questions in interviews, surveys, or focus groups; by observations recorded in field notes; and by examination of documents.

Qualitative data has long been the staple of some social sciences, such as anthropology and history. It is increasingly used in medical education in admissions processes, needs assessments, program development, curriculum evaluation, student performance assessment, and various research and scholarship applications. It provides a source of well-grounded, and often vivid and complex, descriptions and explanations of processes and outcomes – such as students’ and faculty member’s perceptions of experiences outcomes in basic science education. It may be used to supplement, validate, explain, illuminate, and interpret quantitative data. It is often used generate richer understandings of the complexity of processes and outcomes, new conceptual frameworks and new hypotheses to test.

The fundamental procedure for analyzing qualitative data is identifying themes. There are typically multiple reasonable interpretations of themes in any qualitative data set, versus a set of objectively identifiable themes, depending on the perspectives of the investigator. A specific example was used to illustrate this process of data analysis. Methods used to ensure the reliability, validity, and trustworthiness of the analysis include: peer review – review of themes by peer(s) not involved in the research; member checking – sharing interpretations with individuals who were data sources; triangulation – comparing with data from other sources, such as comparing the results of a focus group analysis with results from structured and open-ended questions on surveys; and negative case analysis and saturation – refining a coding scheme until it encompasses all data points.

Qualitative data analysis is an iterative process of: data reduction through coding themes; data display using tables, figures, or narratives; and drawing conclusions which are tested for plausibility by assessing the trustworthiness of the data analysis. Resources were recommended for helping medical science educators to enhance their skills in using qualitative methods, including: the conceptual framework of "grounded theory" (Strauss, Corbin); specific methods for data analysis and display (Miles and Huberman); and specific approaches to data collection (various monographs in the Sage series).

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