CIRA's Qualitative Research Discussion Group (QRDG) Presents “Explaining Complex Outcomes Using Qualitative Data: Pairing Qualitative Analysis and Coincidence Analysis in Implementation Science and Health Services Research”

Talk Overview

Qualitative health research does not necessarily require an outcome (e.g., thematic analysis of implementation barriers and facilitators). Qualitative data can nonetheless play an important role in explaining complex outcomes, accounting for both nuance and context. Matrix displays are one approach for identifying cross-case patterns linking qualitative conditions with outcomes of interest, but complex or large datasets can present methodological challenges. Coincidence analysis has emerged as a new analytic option that allows qualitative researchers to systematically evaluate an entire dataset, large or small, and identify the crucial difference-making conditions. The pairing of two distinct approaches—qualitative analysis and coincidence analysis—allows researchers to generate new types of insights into complex outcomes using qualitative interview data. Several 2025 and 2026 articles from the implementation science and health services literature that feature the QUAL + CNA approach will serve as applied examples throughout the presentation.

Intended Audience: Researchers, fellows, and staff interested in learning about how to pair qualitative and configurational methods

Edward Miech, Indiana University

Edward Miech (EdD) is an Assistant Professor at the Indiana University School of Medicine with over a decade of experience in health services research and implementation science. Over the last six years, he has had the opportunity to collaborate with dozens of project teams in the United States to apply Coincidence Analysis (CNA) to healthcare research, and in the process has become an expert in the approach and published over 50 CNA-related articles since 2020.

CNA is a leading-edge method to analyze how conditions directly link to outcomes and provides a numerical, case-oriented approach that uses applied set theory and Boolean algebra to examine multifactorial causality (i.e., when several conditions together have a joint effect on an outcome).

QRDG Overview:

The goal of the Qualitative Research Discussion Group (QRDG) is to provide opportunities for individuals involved in qualitative or mixed-methods research to meet regularly to discuss the qualitative research process and potentially problem-solve issues that may arise when engaged in qualitative research. Discussion topics include, but are not limited to: logistics, data management, analysis, dissemination, role of the researcher, and ethics. It is intended as a venue for discussing research in progress and new or relevant literature on qualitative methods and practice as well as to create networking opportunities and foster research collaborations. CIRA's Dissemination Implementation Science and Methods (DISM) Core member, Lauretta Grau, PhD, is coordinating the meetings. She can be reached at lauretta.grau@yale.edu.