A large sample factor analysis of the Measures of Criminal Attitudes and Associates in a diverse population of incarcerated offenders.
Bibliographic record
Abstract
Antisocial attitudes and associates are central constructs related to antisocial and criminal behavior. The self-report Measures of Criminal Attitudes and Associates (MCAA) has grown in application within the literature over the past 2 decades. However, tests of the MCAA's factor structure can best be described as preliminary, and there has been no test of measurement invariance. For the present study, we examined the reliability and construct validity of the MCAA in a diverse sample of incarcerated adults in the state of Texas (N = 72,099). Using confirmatory factor analysis, we examined the underlying factor structure and tested for measurement invariance across sex, race/ethnicity, and demand characteristics. Our results supported the original four-factor structure of the MCAA, with measurement invariance being demonstrated across sex (i.e., male vs. female), race/ethnicity (i.e., Black non-Hispanic, White Hispanic, White non-Hispanic), and demand characteristics (i.e., mandated vs. voluntary treatment). Modest associations between the MCAA and criminal history variables were observed, with between-group differences yielding small effect sizes. Overall, our findings provide strong support for the four-factor structure and measurement invariance of the MCAA. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".