Testing the Nonlinearity Assumption Underlying the Use of Reverse-Keyed Items: A Logical Response Perspective
Bibliographic record
Abstract
Researchers often assume a strong, linear relationship between regular- and reverse-keyed items, with responses on regular-keyed items (e.g., agree) perfectly mirroring those on reverse-keyed items (e.g., disagree). The current research challenges this received view and propounds a possible nonlinear relationship, partly due to the logical tendency of midlevel respondents to disagree with both types of items. In four examples (reported human height, job satisfaction, positive-negative affect, and self-esteem; total N = 50,544), a nonlinear model consistently explained additional item variance beyond a linear model. We further demonstrate that this relationship is moderated by item characteristics such as item extremity (job satisfaction) and item softening (self-esteem). Suboptimal modeling of the relationship may result in the apparent bidmensionality of a construct that characterizes regular- and reverse-keyed items as separate factors. User-friendly syntax for the examination of nonlinearity is provided to enhance the accessibility of the procedure.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".