Quantitative Methodology Research: Is it on Psychologists’ Reading Lists?
Bibliographic record
Abstract
Two studies investigated the extent to which researchers are accessing quantitative methodology publications. The first study investigated the number of references to quantitative methodology sources in research articles from six prominent psychology journals. The analyses revealed that 39% of all articles reviewed did not include a quantitative reference of any kind and that 72% contained two or fewer. The second study targeted publications in quantitative methodology journals to determine the frequency with which they were being referenced in non-quantitative publications and other quantitative methodology publications. Results indicate that quantitative methodology articles are being referenced equally by non-quantitative and quantitative methodology researchers, but more importantly, that the number of references to quantitative methodology articles is very low. The results of these studies suggest that researchers are diligent in determining research protocol, procedures, and best practices within their own field, but that researchers are not frequently accessing the quantitative methodology literature to determine the best way to analyze their data. Alternatively, researchers might indeed invest time into determining recent and best statistical procedures, but do not indicate so in the reference section of their work; if this is the case then this paper should be a strong reminder to psychologists about referencing the statistical approaches they utilize.
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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 | 0.319 | 0.796 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.030 | 0.055 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.025 | 0.048 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".