Doctoral training in statistics, measurement, and methodology in psychology: Replication and extension of Aiken, West, Sechrest, and Reno's (1990) survey of PhD programs in North America.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a survey of all PhD programs in psychology in the United States and Canada, the authors documented the quantitative methodology curriculum (statistics, measurement, and research design) to examine the extent to which innovations in quantitative methodology have diffused into the training of PhDs in psychology. In all, 201 psychology PhD programs (86%) participated. This survey replicated and extended a previous survey (L. S. Aiken, S. G. West, L. B. Sechrest, & R. R. Reno, 1990), permitting examination of curriculum development. Most training supported laboratory and not field research. The median of 1.6 years of training in statistics and measurement was mainly devoted to the modally 1-year introductory statistics course, leaving little room for advanced study. Curricular enhancements were noted in statistics and to a minor degree in measurement. Additional coverage of both fundamental and innovative quantitative methodology is needed. The research design curriculum has largely stagnated, a cause for great concern. Elite programs showed no overall advantage in quantitative training. Forces that support curricular innovation are characterized. Human capital challenges to quantitative training, including recruiting and supporting young quantitative faculty, are discussed. Steps must be taken to bring innovations in quantitative methodology into the curriculum of PhD programs in psychology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it