Differential item functioning in a Brazilian–Portuguese version of the Child Perceptions Questionnaire (CPQ<sub>11‐14</sub>)
Bibliographic record
Abstract
Traebert J, de Lacerda JT, Thomson WM, Foster Page L, Locker D. Differential item functioning in a Brazilian–Portuguese version of the Child Perceptions Questionnaire (CPQ 11‐14 ). Community Dent Oral Epidemiol 2010. © 2010 John Wiley & Sons A/S. Abstract – Objective: To determine whether a Portuguese language version of the Child Perceptions Questionnaire for 11–14‐year‐olds (CPQ 11‐14 ) showed differential item functioning (DIF) when compared with the original English language version. Methods: CPQ 11‐14 data from a school‐based Brazilian study ( n = 138) was compared with CPQ 11‐14 data collected as part of a school‐based study conducted in New Zealand ( n = 322). In order to detect DIF, ordinal logistic regression analysis was performed with each CPQ 11‐14 item as the dependent variable. The independent variables were language group (English versus Portuguese), the CPQ 11‐14 sub‐scale score of which the item was a part, and an interaction term for language*sub‐scale score. Nonuniform DIF was deemed to be present if the interaction term was significant. Moderate to large uniform DIF was deemed to be present if after removing the interaction term the β coefficient (log odds ratio) for language group was significant and numerically greater than 0.64. Analyses were also undertaken to detect pseudo‐DIF. Results: Nonuniform DIF was found in five items and moderate to large uniform DIF in an additional four items. Analyses using ‘purified’ sub‐scale scores indicated that little of the DIF detected was pseudo‐DIF. A comparison of the language groups using DIF affected and DIF‐free overall and subscale CPQ 11‐14 scores revealed that the DIF detected had only a marginal effect on the differences between language groups in scores. Conclusion: Oral health‐related quality of life questionnaires, particularly those that have been translated, need to be assessed for DIF and its likely impact on group comparisons.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".