Factor analytic replication and model comparison of the BASC-2 Behavioral and Emotional Screening System.
Bibliographic record
Abstract
We conducted this study to add to literature of previous conflicting factorial examinations of the BASC-2 Behavioral and Emotional Screening System (BESS), Teacher Form-Child/Adolescent. Data were collected by an urban school district in the southeastern United States including 2,228 students rated by 120 teachers in Fall 2014 and 1,955 students rated by 104 teachers in Spring 2015. In both samples, we replicated and then conceptually and statistically compared factor models to examine the (a) 4-factor structure from which the BESS Teacher Form was developed, and (b) existence of a general factor currently being used. Previous studies examined the 4-factor and bifactor structure of the BESS Teacher Form on separate samples. Our model comparison results support a multidimensional interpretation. We recovered similar fit statistics and standardized factor loadings as previous factor analyses. However, measures of variance accounted for by the general factor were below recommended thresholds of a unidimensional construct. We recommend advancing a factorial model that represents a weighted combination of general and specific factors, but do not support continued use of a unidimensional total T score. Limitations and implications of the study are discussed. (PsycINFO Database Record
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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 | 0.054 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".