Abstract
Person-fit analysis revolves around fitting an item response theory (IRT) model to respondents’ vectors of item scores on a test and drawing statistical inferences about fit or misfit of these vectors. Four person-fit measures were studied in order-restricted latent class models (OR-LCMs). To decide whether the OR-LCM fits an item score vector, a Bayesian framework was adopted and posterior predictive checks were used. First, simulated Type I error rates and detection rates were investigated for the four person-fit measures under varying test and item characteristics. Second, the suitability of the OR-LCM methodology in a nonparametric IRT context was investigated. The result was Type I error rates close to the nominal Type I error rates and detection rates close to the detection rates found in OR-LCMs. This means that the OR-LCM methodology is a suitable alternative for assessing person fit in nonparametric IRT models.
Original language | Undefined |
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Pages (from-to) | 459-478 |
Number of pages | 20 |
Journal | Applied psychological measurement |
Volume | 27 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2003 |
Keywords
- order-restricted latent class analysis
- nonparametric item response theory
- posterior predictive checks
- person-fit analysis
- person-fit statistics
- METIS-215721
- Bayesian approach to person fit
- IR-60150