Assessing and interpreting interaction effects: A reply to Vancouver, Carlson, Dhanani, and Colton (2021).
Bibliographic record
Abstract
Van Iddekinge et al. (2018)'s meta-analysis revealed that ability and motivation have mostly an additive rather than an interactive effect on performance. One of the methods they used to assess the ability × motivation interaction was moderated multiple regression (MMR). Vancouver et al. (2021) presented conceptual arguments that ability and motivation should interact to predict performance, as well as analytical and empirical arguments against the use of MMR to assess interaction effects. We describe problems with these arguments and show conceptually and empirically that MMR (and the ΔR and ΔR2 it yields) is an appropriate and effective method for assessing both the statistical significance and magnitude of interaction effects. Nevertheless, we also applied the alternative approach Vancouver et al. recommended to test for interactions to primary data sets (k = 69) from Van Iddekinge et al. These new results showed that the ability × motivation interaction was not significant in 90% of the analyses, which corroborated Van Iddekinge et al.'s original conclusion that the interaction rarely increments the prediction of performance beyond the additive effects of ability and motivation. In short, Van Iddekinge et al.'s conclusions remain unchanged and, given the conceptual and empirical problems we identified, we cannot endorse Vancouver et al.'s recommendation to change how researchers test interactions. We conclude by offering suggestions for how to assess and interpret interactions in future research. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.083 | 0.346 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.052 | 0.117 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".