Publication trends for qualitative inquiry in American Psychological Association and Association for Psychological Science journals.
Bibliographic record
Abstract
Framed against the long-standing dominance of quantitative methods in psychological science, this study examined contemporary publishing patterns for qualitative inquiry in American Psychological Association (APA) and Association for Psychological Science (APS) journals. We examined 19,012 publications across 95 APA and APS journals across four time points (2005, 2012, 2019, and 2022). The percentage of qualitative articles was determined using the methodology field value within APA PsycInfo, a process that we validated through a batch test. We also conducted a content analysis of journal mission statements and submission guidelines, and we made comparisons in light of journal impact factors. Our findings show a nearly threefold increase in qualitative publications accelerating over time from 2005 to 2022, albeit with wide variations depending on the type of journal. Qualitative-friendly journals were more likely to be published by APA, be specialty journals, be dedicated to diverse populations, and have lower impact factors. Conversely, qualitative research was less likely to be published in APS journals, core psychology journals, journals focused on general populations, and journals with higher impact factors (with some notable exceptions). We discuss these findings in terms of implications for the advancement of psychological science, including the discipline's need for development in qualitative training and expertise, its commitments to antiracism and anticolonialism, its fragmentation, and its equity in publishing. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.105 | 0.345 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.057 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".