Increasing literacy in quantitative methods: The key to the future of Canadian psychology.
Bibliographic record
Abstract
Quantitative methods (QM) dominate empirical research in psychology. Unfortunately most researchers in psychology receive inadequate training in QM. This creates a challenge for researchers who require advanced statistical methods to appropriately analyze their data. Many of the recent concerns about research quality, replicability, and reporting practices are directly tied to the problematic use of QM. As such, improving quantitative literacy in psychology is an important step towards eliminating these concerns. The current paper will include two main sections that discuss quantitative challenges and opportunities. The first section discusses training and resources for students and present descriptive results on the number of quantitative courses required and available to graduate students in Canadian psychology departments. In the second section, we discuss ways of improving quantitative literacy for faculty, esearchers, and clinicians. This includes a strong focus on the importance of collaboration. The paper concludes with practical recommendations for improving quantitative skills and literacy for students and researchers in Canada.
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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 | 0.356 | 0.556 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".