A Delphi study to strengthen research methods training in undergraduate psychology programmes
Bibliographic record
Abstract
Psychology programmes often emphasise inferential statistical tests over a solid understanding of data and research design. This imbalance may leave graduates under-equipped to effectively interpret research and employ data to answer questions. We conducted a consensus process in the form of a two round modified-Delphi study. Our goal was to identify the research methods skills that the UK psychology community deems essential for undergraduates to learn. Participants included 103 research methods instructors, academics, students, and non-academic psychologists. Of 78 items included in the consensus process, 34 reached consensus. Coupled with a qualitative analysis of 707 open-ended text responses, we developed nine recommendations for organisations that accredit undergraduate psychology programmes—such as the British Psychological Society (BPS). We recommend emphasising (1) data skills, (2) research design, (3) descriptive statistics, (4) critical analysis, (5) both significance testing and parameter estimation, and (6) qualitative methods; as well as (7) giving precedence to foundational skills, (8) promoting transferable skills, and (9) creating space within curricula to enable these recommendations. Our data and findings can help modernise accreditation standards to include clearly-defined, assessable skills that have broad acceptance and foster a competent graduate body for the contemporary world.
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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 | 0.183 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".