Deductive & Inductive Thematic Analysis and Frequencies applied to Interviews on Data Linkage: A Mixed Methods Approach

Deductive & Inductive Thematic Analysis and Frequencies applied to Interviews on Data Linkage: A Mixed Methods Approach
Deductive & Inductive Thematic Analysis and Frequencies applied to Interviews on Data Linkage: A Mixed Methods Approach

Principal speaker

Dr Judy Rose

This seminar provides insights into research that combines thematic analysis and frequencies in a mixed methods analysis to support a study on data linkage. The study conducted interviews with 12 experts about the benefits and risks of a range of big data technologies, focussing on data linkage. A thematic analysis method was used to organise the interview data into units of meaning that became themes and subthemes. To code the data "a priori' codes were derived via deductive processes from the set of semi-structured interview questions. Next, emergent codes were created via inductive processes to include new or unexpected topics or themes. Counting of codes showed a slightly higher number of emergent (41) themes, compared to "a priori' themes (38). Theme coverage was quantified to indicate relative "airtime' given to topics by all interviewees and then compared across themes. The findings indicated the most common theme "trust, privacy and sharing' was raised by all 12 experts a total of 42 times. A related subtheme "Indigenous data sovereignty' was raised by 1 expert a total of 7 times. The application of mixed methods helped us to determine which themes were dominant (via use counts and percentage coverage). This meant we were able to understand more clearly what kinds of issues different experts found important or to help detect sample bias (e.g., being pro or anti data linkage).

Format: This seminar will be delivered online during a 1.5-hour period, via Collaborate. The last half hour will be a Question-and-Answer session.

Pre-requisites: None

Relationship to RED workshops and seminars: This seminar is a companion to the "Mixing Clustering in with Thematic Analysis of Expert Interviews about Data Linkage' that will be presented by A/Professor Sama Low-Choy.

Other related RED training: This seminar builds on the foundations provided by the six Mixed Methods Foundations workshops (MM1 - MM6). It is related to other RED training on mixed methods including: "Interpretive Phenomenology as applied to Interview Data in QUAL-dominant MM', "Integrating Hermeneutic Phenomenology in QUAL-Dominant MM', and "Interviewing to Quantify Expert Knowledge with Uncertainty'.

Acknowledgements: This seminar presents methods used in a study funded by an Australian Research Council Linkage Learned Academies Special Projects (LASP) grant with the Academy of Social Sciences in Australia. This work is to be published as a book chapter, "Strategic Data Linkage to Improve the Wellbeing of Vulnerable Children" (Rose, J., Low-Choy, S., Katz, I. & Homel, R.) in Chan, J. & Saunders P. (Eds.). The use of "Smart" Data for Social Policy: Benefits and Risks. Academy of the Social Sciences in Australia (ASSA), Canberra.

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RSVP on or before Monday 7 June 2021 15.32 pm, by email RED@griffith.edu.au , or via https://events.griffith.edu.au/n9AQxY

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