Bibliographic record
Abstract
Psychological research is rife with inappropriately concluding “no effect” between predictors and outcome in regression models following statistically nonsignificant results. This approach is methodologically flawed, however, because failing to reject the null hypothesis using traditional, difference-based tests does not mean the null is true. Using this approach leads to high rates of incorrect conclusions which floods psychological literature. This thesis introduces a novel, methodologically sound alternative; I demonstrate how to apply equivalence testing to evaluate whether predictors have negligible effects on the outcome in multiple regression. I constructed a simulation study to evaluate the performance of two equivalence-based methods and compared it to the traditional test. I further developed two R functions which accompany this thesis to supply researchers with open-access and easy-to-use tools. The use of the proposed equivalence-based methods and R functions is illustrated through examples from the literature, and recommendations for results reporting and interpretations are discussed.
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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 | 0.137 | 0.564 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".