Expanding Statistical Horizons: Supplementary Training Trends in a Sample of North American Psychology Researchers
Bibliographic record
Abstract
Statistics skills are crucial for researchers in psychology, but research suggests that graduate training is insufficient to meet modern quantitative research demands. Consequently, researchers tend to seek training outside of their current or former graduate program. However, little is known regarding the frequency of use and perceptions surrounding various supplementary statistical resources. This study aimed to investigate how psychology researchers acquire statistical training beyond their graduate program, how necessary and useful they find various resources, and if there were differences based on student status. The final sample included 280 academic researchers in Canada or the United States. Participants engaged with statistical content more frequently than limited prior research suggests. We found that online resources and Quantitative Methods (QM) papers were the most frequently used, statistical consultants had the highest perceived usefulness, and QM papers had high perceived necessity. Student researchers, on average, rated most of the statistical resources as more necessary than researchers with a PhD, with notable differences for online resources and statistical consultants. Usefulness ratings and frequency of use, however, were fairly similar across the groups. Implications and recommendations for students, researchers, instructors, and program administrators are discussed. Data and study materials can be found at https://osf.io/673kj/.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".