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Record W3037305412 · doi:10.1037/pas0000883

Using item response theory to evaluate the Children’s Behavior Questionnaire: Considerations of general functioning and assessment length.

2020· article· en· W3037305412 on OpenAlexaff
David A. Clark, M. Brent Donnellan, C. Emily Durbin, Rebecca J. Brooker, Tricia K. Neppl, Megan R. Gunnar, Stephanie M. Carlson, Lucy Le Mare, Grazyna Kochanska, Philip A. Fisher, Leslie D. Leve, Mary K. Rothbart, Samuel P. Putnam

Bibliographic record

VenuePsychological Assessment · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsSimon Fraser University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental HealthNational Institute on Alcohol Abuse and AlcoholismNorthwestern University
KeywordsPsycINFOTemperamentPsychologyItem response theoryPsychometricsDevelopmental psychologyScale (ratio)Clinical psychologyPersonalitySocial psychologyMEDLINE

Abstract

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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).

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.104
GPT teacher head0.418
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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