Thinking through and designing qualitative research studies: a focused mapping review of 30 years of qualitative research in sport psychology
Bibliographic record
Abstract
Amid the increasing use of qualitative methods in the field of sport psychology, a number of researchers have initiated discussions about issues of rigour and quality in qualitative inquiry. Methodological coherence has been offered as an approach to strengthening qualitative inquiry by ensuring that the elements of qualitative research are appropriately aligned. This study presents a focused mapping review and synthesis of the methodological coherence of qualitative sport psychology research published in five peer-reviewed journals over 30 years. 710 articles were subjected to an in-depth analysis. The philosophical position, methodology, data generation and analysis methods, presentation of findings, description of quality, statement of researcher position, and methodological coherence of each article were coded. Results indicated that post-positivist or critical realist approaches are dominant in sport psychology, although there has been an increase in the variety of philosophical positions employed over time. Further, the majority of articles in sport psychology were methodologically coherent. We conclude by forwarding practical considerations for thinking through and designing methodologically coherent qualitative studies in sport psychology, which are intended to be accessible for all researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.212 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".