Psychometric Properties of a Preschool Language, Literacy, and Behavior Screener
Bibliographic record
Abstract
This study investigated the psychometric properties of the Preschool Language, Literacy, and Behavior Screener (PLLB-S). We examined and tested the factor structure of the PLLB-S using exploratory and confirmatory factor analyses. We further conducted internal consistency, concurrent validity, and predictive validity analyses and evaluated teacher satisfaction using PLLB-S. Our factor analyses resulted in 22 items distributed among three subscales with high internal consistency: Oral language, emergent literacy, and behavior skills. The PLLB-S and its subscales correlated moderately to strongly with standardized measures. The emergent literacy of the PLLB-S was the only subscale that significantly predicted children’s later vocabulary knowledge. Preschool teachers reported high satisfaction with the content and purpose of the questionnaire. We concluded that this tool with sound psychometric properties can potentially help increase the feasibility and efficiency of implementing standardized assessments in MTSS frameworks in preschool classrooms.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 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".