Item Response Theory and Measurement Invariance Investigations of the Youth Level of Service/Case Management Inventory (YLS/CMI)
Bibliographic record
Abstract
Derived from the Risk-Need-Responsivity framework, the Youth Level of Service/Case Management Inventory (YLS/CMI) is a widely implemented instrument used to assess risk of recidivism in justice-involved youth. Its results inform risk classification, sentencing decisions, and treatment planning. Given the impact of the YLS/CMI on the lives of justice-involved youth, its validity needs to be thoroughly investigated. This dissertation is comprised of three studies examining the psychometric properties of the YLS/CMI based on samples of community-sentenced youth in Ontario, Canada. Study 1 examined the YLS/CMI items and its internal structure using item response theory (IRT) analyses and confirmatory factor analysis. The YLS/CMI items loaded onto their respective domains and the domains in turn converged onto a single factor as theorized in the RNR framework. The IRT analyses demonstrated that the instrument was most informative for youth with average risk levels, with items from the Personality, Attitude, and Criminal History domains especially adept at discriminating between individuals with varying risk levels. Study 2 explored the measurement invariance of the YLS/CMI across gender (male vs. female) and race (Black vs. White) using differential item functioning (DIF) analyses within an IRT framework. Most of the items demonstrated similar discrimination across subgroups, with the exception of verbally aggressive across gender, and poor relations with father across race. Several items demonstrated differential likelihood of endorsement across gender and race. Study 3 examined the measurement invariance of the YLS/CMI across a sample of Indigenous and non-Indigenous justice-involved youth using DIF. Results demonstrated similar discrimination of the items across groups, with the exception of poor frustration tolerance. Items from the Education domain were more likely to be endorsed for non-Indigenous youth, while items from the Substance Abuse domain were more likely to be endorsed for Indigenous youth. Results also indicated that the total scores were not predictive of recidivism for Indigenous youth. Findings from this dissertation highlight the importance of investigating the psychometric properties of risk assessment instruments in order to establish its validity and understand its implications for justice-involved individuals.
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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 | 0.059 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".