Factor analytic replication and model comparison of the BASC-2 Behavioral and Emotional Screening System.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.003 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it