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Abstract

Framework analysis and applied qualitative research can be a perfect match, in large part because framework analysis was developed for the explicit purpose of analyzing qualitative data in applied policy research. Framework analysis is an inherently comparative form of thematic analysis which employs an organized structure of inductively- and deductively-derived themes (i.e., a framework) to conduct cross-sectional analysis using a combination of data description and abstraction. The overall objective of framework analysis is to identify, describe, and interpret key patterns within and across cases of and themes within the phenomenon of interest. This flexible and powerful method of analysis has been applied to a variety of data types and used in a range of ways in applied research. Framework analysis consists of two major components: creating an analytic framework and applying this analytic framework. This paper details the five steps in framework analysis (data familiarization, framework identification, indexing, charting, and mapping and interpretation) through conducting secondary analysis on this special issue’s common dataset. This worked example adds to the existing framework analysis methodology literature both through describing the analysis specifics and through highlighting the importance of multiple considerations of units of analysis. This paper also includes reflection on the myriad reasons that framework analysis is valuable for applied research.

Keywords

analytic framework, applied qualitative research, framework analysis

Author Bio(s)

Laurie J. Goldsmith, PhD, is the Founder and Principal of GoldQual Consulting, a qualitative and mixed methods health services research company, and an Adjunct Professor in Simon Fraser University’s Faculty of Health Sciences. Her research interests include access to health care and documenting and advancing the use of research methods. Please direct correspondence to laurie_goldsmith@sfu.ca.

Publication Date

6-22-2021

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.

DOI

10.46743/2160-3715/2021.5011

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