Dataset II_NVivo codebook of staff interview transcripts about faculty development for online teaching_v1 01-06-2023.docx
This NVivo codebook relates to interview transcripts collected from teaching staff interviews (dataset II, see ESM C) conducted to capture a holistic view of the teaching staff's perceptions of all the Faculty Development (FD) available and how they have applied any knowledge or skills acquired from them. We emailed an online survey (dataset I, see ESM B) to the Faculty of Medical and Health Sciences (FMHS) University of Auckland teaching community, comprising approximately 460 staff members. We included the invitation to an interview by a link at the end of the survey. We went through an iterative approach to refining the interview question guide with our external consulting agency (Academic Consulting LTD).
Qualitative methods sampling continues until data saturation—when no new information emerges from the collected data—which, according to Ando et al. (Ando et al. 2014), often occurs with 12 participants in a relatively homogenous sample. We stopped interview recruitment at 20 participants.
Academic Consulting LTD then used a qualitative software package QSR NVivo Software to support data analysis of the interview transcripts. We first undertook inductive analysis, following Braun and Clarke's (2012) practical six-phase approach to thematic analysis. The phases are 1) Familiarizing researchers with data, 2) Generating initial codes, 3) Searching for themes, 4) Reviewing themes, 5) Defining and naming themes, and 6) Producing the report. We then performed deductive analysis to check the salience of findings across the datasets as part of the triangulation process.
This is underlying data associated with the article 'Faculty development for strengthening online teaching capability: being responsive to what staff want, evaluated with Kirkpatrick’s model of teaching effectiveness' which has been published in MedEdPublish 2023, 13:127 (https://doi.org/10.12688/mep.19692.1).