Building Transparency and Trustworthiness in Inductive Research Through Computer-Aided Qualitative Data Analysis Software

Abstract

Many scholars have called for qualitative research to demonstrate transparency and trustworthiness in the data analysis process. Yet these processes, particularly within inductive research, often remain shrouded in mystery. We suggest that computer-aided/assisted qualitative data analysis software (CAQDAS) can support qualitative researchers in their efforts to present their analysis and findings in a transparent way, thus enhancing trustworthiness. To this end, we propose, describe, and illustrate working examples of six CAQDAS building blocks, three combined CAQDAS techniques, and two coder consistency checks. We argue that these techniques give researchers the language to write about their methods and findings in a transparent manner and that their appropriate use enhances a research project’s trustworthiness. Specific CAQDAS techniques are rarely discussed across an array of inductive research processes. Thus, we see this article as the beginning of a conversation about the utility of CAQDAS to support inductive qualitative research.

Business Education: Filling the Gaps in the Leader’s Awareness Concerning Organizational Phronesis

“…We analyzed it with computer-aided qualitative data analysis software-CAQDAS to increase efficiency and effectiveness. We used the software NVivo [60,61]. We categorized and coded the excerptions [52,53,60,61].…”

“…We used the software NVivo [60,61]. We categorized and coded the excerptions [52,53,60,61]. We classified relevant interventions for the discussion according to the literature.…”

“…After coding, we conducted a careful checking of coding with a "Compound Query" [60]. We ran a code retrieval (coding stripes) to support and clarify the codebook [61]. Nevertheless, there are synonymous words and expressions that NVivo cannot discover.…”

“…Nevertheless, there are synonymous words and expressions that NVivo cannot discover. Consequently, before and after the coding process, we did a line-by-line reading to ensure the analysis's depth and robustness [61].…”

“…The first analysis we did with NVivo was "Word Frequency Query" [60,61]. It used the succeeding selection criteria a) with stemmed words, b) 100 most frequent words, and c) with "three" minimum length by default.…”