Investigating the Impact of Item Parameter Drift for Item Response Theory Models with Mixture Distributions

Investigating the Impact of Item Parameter Drift for Item Response Theory Models with Mixture Distributions

This study investigates the impact of item parameter drift (IPD) on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT) models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-worl...

Randomized Item Response Theory Models

The randomized response (RR) technique is often used to obtain answers on sensitive questions. A new method is developed to measure latent variables using the RR technique because direct questioning leads to biased results. Within the RR technique is the probability of the true response modeled by an item response theory (IRT) model. The RR technique links the observed item response with the tr...

Longitudinal Effects of Item Parameter Drift

Differential item functioning magnitude and impact measures from item response theory models.

Measures of magnitude and impact of differential item functioning (DIF) at the item and scale level, respectively are presented and reviewed in this paper. Most measures are based on item response theory models. Magnitude refers to item level effect sizes, whereas impact refers to differences between groups at the scale score level. Reviewed are magnitude measures based on group differences in ...

Parameter Estimation with Mixture Item Response Theory Models: A Monte Carlo Comparison of Maximum Likelihood and Bayesian Methods