Using Qualitative Comparative Analysis of Key Informant Interviews in Health Services Research

Enhancing a Study of Adjuvant Therapy Use in Breast Cancer Care

Dados Bibliográficos

ID9099697
AutoresAnn Scheck Mcalearney (0000-0001-9107-5419, Ohio Department of Health), Daniel Walker (0009-0001-7703-5936), Daniel M Walker (0000-0001-5434-3162, Institute of Oriental Manuscripts), Alexandra D Moss, Alexandra Moss (Institute of Oriental Manuscripts), Nina A Bickell (0000-0003-1102-8677, Institute of Oriental Manuscripts)
Ano2016
Volume54
Fascículo4
Páginas400-405
Data de publicação2016-04-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoMedical Care (JOURNAL)
Identificadores do periódicoISSN: 0025-7079 • E-ISSN: 1537-1948
EditoraOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000503
PMID26908085
OpenAlexW2287519663
IdiomaEN
Citações recebidas6
Referências citadas13

BACKGROUND: Qualitative comparative analysis (QCA) is a methodology created to address causal complexity in social sciences research by preserving the objectivity of quantitative data analysis without losing detail inherent in qualitative research. However, its use in health services research (HSR) is limited, and questions remain about its application in this context. OBJECTIVE: To explore the strengths and weaknesses of using QCA for HSR. RESEARCH DESIGN: Using data from semistructured interviews conducted as part of a multiple case study about adjuvant treatment underuse among underserved breast cancer patients, findings were compared using qualitative approaches with and without QCA to identify strengths, challenges, and opportunities presented by QCA. SUBJECTS: Ninety administrative and clinical key informants interviewed across 10 NYC area safety net hospitals. MEASURES: Transcribed interviews were coded by 3 investigators using an iterative and interactive approach. Codes were calibrated for QCA, as well as examined using qualitative analysis without QCA. RESULTS: Relative to traditional qualitative analysis, QCA strengths include: (1) addressing causal complexity, (2) results presentation as pathways as opposed to a list, (3) identification of necessary conditions, (4) the option of fuzzy-set calibrations, and (5) QCA-specific parameters of fit that allow researchers to compare outcome pathways. Weaknesses include: (1) few guidelines and examples exist for calibrating interview data, (2) not designed to create predictive models, and (3) unidirectionality. CONCLUSIONS: Through its presentation of results as pathways, QCA can highlight factors most important for production of an outcome. This strength can yield unique benefits for HSR not available through other methods

Context (archaeology) · Data mining · Data science · Identification (biology) · Machine learning · Management science · Objectivity (philosophy) · Presentation (obstetrics) · Qualitative comparative analysis · Qualitative research · Sociology · Strengths and weaknesses · Advanced Causal Inference Techniques · Computer Science · Critical Realism in Sociology · Engineering · Medicine · Psychology · Qualitative Comparative Analysis Research · Social Psychology