Discussion for SRQR item: Data analysis

Data analysis

Describe your analytic process as transparently as possible.1

If you used an approach that has a well-defined process for data analysis (e.g., grounded theory, discourse analysis, phenomenography):

Specify the unit of analysis.2

Explain the rationale underlying different decisions made throughout the data analysis process to provide as much transparency as possible.3

If observations that contrast or deviate from identified concepts or themes were important to your analysis, describe how these discrepancies were handled during the analysis.

If you drew upon a theoretical perspective or framework during analysis, describe theoretical or other influences on your analysis scheme or categories if they exist. If you identified a theoretical perspective or framework early in the conception of the study or after reviewing some or all of their data, consider referring to these as “sensitizing concepts” to acknowledge that the approach is inductive, but with influence from relevant theory, models, or organizational schemes. Alternatively, explain that themes were developed from the data with no external influences.

Describe which members of the research team are involved in data analysis and what perspective(s) they bring to the analysis.

If software was used to assist with data analysis4, describe how it was used. Simply stating that software was used is insufficient.

Jump to:

Why readers need this information

Techniques used for data analysis will depend on the paradigm, approach, and/or data collection methods selected by the researchers. Correspondingly, authors should be as transparent as possible about the analytic process so that readers can follow the logic of inquiry from the research question(s) to the analysis and findings.

Examples

…we brought sensitizing concepts to the analysis while we conducted an open, inductive analysis.[REF]In this case the sensitizing concepts arose, a priori to analysis, from a framework derived from the literature [REF] (as described above), in which participants’ motivations to act are based on principles of professionalism, internal affect, or potential implications of their actions.[REF]

Through an iterative process of listening, discussing, and relistening, the team identified and consensually validated emerging themes[REF] and appended segments of dialogue supporting the proposed themes. Recruitment stopped when saturation was reached (no new themes were identified). The team systematically reviewed the themes and sorted them into content domains. The team used an analytic matrix to identify patterns and connections amongst the domains. Two of us not involved in the qualitative coding process (R.E., M.K.) audited the analytic matrix, choice of quotes, and thematic analysis.

The analysis started after the first interview. All data were analyzed with the aid of the audio- coding facility of the NVivo 8: QSR International Pty Ltd, Doncaster, Vic, Australia programme. First, [name] and [name] coded independently from one another, making sure to stay semantically close to the participants’ wording. Then we discussed these open codes and defined axial codes.[REF] New insights about the impact of CST were written down in memos.

Videotapes were analysed using immersion/crystallisation methods of qualitative data analysis.[REF] With no pre-existing framework developed in advance for analysis, an inductive approach was used to discover patterns of NVB in the data. A team of six researchers met weekly for 18 months to view videos together. Using a consensus-building approach based on a combination of field notes, ‘opportunistic’ interviews with the participants, and repeated viewing of the same material, sometimes many months apart, we eventually achieved consensus on verbal, non-verbal, and physical themes and patterns observed in the data. Finally, as a test of ‘goodness-of-fit,’ we carefully reviewed the videotapes for any ‘deviant’ cases that did not fit the categories we had developed.

All transcripts were coded thematically by four of the five authors, who met regularly to identify areas of convergence until full agreement was reached. One of the interviewers (P.M.) maintained an audit trail to track the team’s developing thinking. A process of dialectical empiricism[REF] was used to categorise the emergent themes into more abstract concepts…

Back to top

Footnotes

  1. E.g., characterize the processes and decisions made for initial classification or segmentation of data, pattern identification and description, and/or development of in-depth interpretations.↩︎

  2. In qualitative research, the unit of analysis is not necessarily the same as the unit of sampling (e.g., individual participants or events). Instead, some approaches use specific events as the unit of analysis, such as mentions of a particular topic or experience, or observations of a particular behavior or phenomenon, while others use groups rather than individual group participants. This specification has implications for how the data are organized and analyzed as well as the inferences drawn from the data.↩︎

  3. In some approaches researchers use memoing or bracketing to make their reflections, interpretations, and links among passages explicit and more transparent to others.↩︎

  4. e.g., used to apply codes after the final coding scheme was developed; to extract coded passages for further synthesis and identification of themes; or to identify passages with key words↩︎