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DTSTART:20241103T020000
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DESCRIPTION: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 rol
 e in explaining complex outcomes\, accounting for both nuance and context
 . Matrix displays are one approach for identifying cross-case patterns li
 nking qualitative conditions with outcomes of interest\, but complex or l
 arge 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 iden
 tify the crucial difference-making conditions. The pairing of two distinc
 t approaches—qualitative analysis and coincidence analysis—allows researc
 hers to generate new types of insights into complex outcomes using qualit
 ative interview data. Several 2025 and 2026 articles from the implementat
 ion science and health services literature that feature the QUAL + CNA ap
 proach will serve as applied examples throughout the presentation. Intend
 ed Audience: Researchers\, fellows\, and staff interested in learning abo
 ut how to pair qualitative and configurational methods Edward Miech\, Ind
 iana University Edward Miech (EdD) is an Assistant Professor at the India
 na University School of Medicine with over a decade of experience in heal
 th 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 re
 search\, and in the process has become an expert in the approach and publ
 ished over 50 CNA-related articles since 2020. CNA is a leading-edge meth
 od to analyze how conditions directly link to outcomes and provides a num
 erical\, case-oriented approach that uses applied set theory and Boolean 
 algebra to examine multifactorial causality (i.e.\, when several conditio
 ns together have a joint effect on an outcome). QRDG Overview: The goal o
 f the Qualitative Research Discussion Group (QRDG) is to provide opportun
 ities for individuals involved in qualitative or mixed-methods research t
 o meet regularly to discuss the qualitative research process and potentia
 lly problem-solve issues that may arise when engaged in qualitative resea
 rch. Discussion topics include\, but are not limited to: logistics\, data
  management\, analysis\, dissemination\, role of the researcher\, and eth
 ics. It is intended as a venue for discussing research in progress and ne
 w or relevant literature on qualitative methods and practice as well as t
 o create networking opportunities and foster research collaborations. CIR
 A's Dissemination Implementation Science and Methods (DISM) Core member\,
  Lauretta Grau\, PhD\, is coordinating the meetings. She can be reached a
 t lauretta.grau@yale.edu .\n\nSpeaker:\nEdward Miech\n\nAdmission:\nFree\
 n\nDetails URL:\nhttps://medicine.yale.edu/event/cira-qualitative-researc
 h-discussion-group-edward-miech/\n
DTEND;TZID=America/New_York:20260918T130000
DTSTAMP:20260912T144959Z
DTSTART;TZID=America/New_York:20260918T120000
LOCATION:URL: https://yale.zoom.us/j/93012332332
SEQUENCE:0
STATUS:Confirmed
SUMMARY:CIRA's Qualitative Research  Discussion Group (QRDG) Presents “Exp
 laining Complex Outcomes Using Qualitative Data: Pairing Qualitative Anal
 ysis and Coincidence Analysis in Implementation Science and Health Servic
 es Research”
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