Using item response theory to evaluate the Children’s Behavior Questionnaire: Considerations of general functioning and assessment length.
Bibliographic record
Abstract
Although the Children's Behavior Questionnaire (CBQ; Rothbart, Ahadi, Hershey, & Fisher, 2001) is the most popular assessment for childhood temperament, its psychometric qualities have yet to be examined using Item Response Theory (IRT) methods. These methods highlight in detail the specific contributions of individual items for measuring different facets of temperament. Importantly, with 16 scales for tapping distinct aspects of child functioning (195 items total), the CBQ's length can be prohibitive in many contexts. The detailed information about item functioning provided by IRT methods is therefore especially useful. The current study used IRT methods to analyze the CBQ's 16 temperament scales and identify potentially redundant items. An abbreviated "IRT form" was generated based on these results and evaluated across four independent validation samples. The IRT form was compared to the original and short CBQ forms (Putnam & Rothbart, 2006). Results provide fine-grained detail on the CBQ's psychometric functioning and suggest it is possible to remove up to 39% of the original form's items while largely preserving the measurement precision and content coverage of each scale. This study provides considerable psychometric information about the CBQ's items and scales and highlights future avenues for creating even more efficient high-quality temperament assessments. (PsycInfo Database Record (c) 2020 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 | 0.092 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".