Examining the Effect of Reverse Worded Items on the Factor Structure of the Need for Cognition Scale
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Reverse worded (RW) items are often used to reduce or eliminate acquiescence bias, but there is a rising concern about their harmful effects on the covariance structure of the scale. Therefore, results obtained via traditional covariance analyses may be distorted. This study examined the effect of the RW items on the factor structure of the abbreviated 18-item Need for Cognition (NFC) scale using confirmatory factor analysis. We modified the scale to create three revised versions, varying from no RW items to all RW items. We also manipulated the type of the RW items (polar opposite vs. negated). To each of the four scales, we fit four previously developed models. The four models included a 1-factor model, a 2-factor model distinguishing between positively worded (PW) items and RW items, and two 2-factor models, each with one substantive factor and one method factor. Results showed that the number and type of the RW items affected the factor structure of the NFC scale. Consistent with previous research findings, for the original NFC scale, which contains both PW and RW items, the 1-factor model did not have good fit. In contrast, for the revised scales that had no RW items or all RW items, the 1-factor model had reasonably good fit. In addition, for the scale with polar opposite and negated RW items, the factor model with a method factor among the polar opposite items had considerably better fit than the 1-factor model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it