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 two-round modified-Delphi 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. We coupled these results with a qualitative analysis of 707 open-ended text responses to develop nine recommendations for organisations that accredit undergraduate psychology programmes—such as the British Psychological Society (BPS). We recommend that accreditation standards emphasise (1) data skills, (2) research design, (3) descriptive statistics, (4) critical analysis, (5) qualitative methods, and (6) both parameter estimation and significance testing; as well as (7) give precedence to foundational skills, (8) promote transferable skills, and (9) create space in curricula to enable these recommendations. Our data and findings can inform modernised accreditation standards to include clearly-defined, assessable, and widely-encouraged skills that foster a competent graduate body for the contemporary world.
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How this classification was reachedexpand
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 | 0.065 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".