How to Test Questions About Similarity in Personality and Social Psychology Research
Bibliographic record
Abstract
Social and personality psychologists are often interested in the extent to which similarity, agreement, or matching matters. The current article describes response surface analysis (RSA), an approach designed to answer questions about how (mis)matching predictors relate to outcomes while avoiding many of the statistical limitations of alternative, often-used approaches. We explain how RSA provides compressive and often more valid answers to questions about (mis)matching predictors than traditional approaches provide, outline steps on how to use RSA (including modifiable syntax), and demonstrate how to interpret RSA output with an example. To bolster our argument that RSA overcomes many limitations of traditional approaches (i.e., incomplete or misleading inferences), we compare results from four popular approaches (i.e., difference scores, residuals, moderated regression, and the truth and bias model) to those obtained from RSA. We discuss specific applications of RSA to social and personality psychology research.
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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 | 0.156 | 0.552 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".