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Accuracy of Rasch model item parameter estimation
Assessment and Reporting
  • Luc T Le, ACER
  • Ray J Adams, ACER and University of Melbourne
Publication Date

This study used Monte Carlo simulations to evaluate the item parameter recovery from ACER ConQuest 3 software (Adams, Wu, & Wilson, 2012) for the dichotomous Rasch model. The authors’ primary focus was the comparison of its estimation methods, joint maximum likelihood (JML), marginal maximum likelihood (MML) with a normal distribution assumption and MML with a discrete distributions assumption when the populations were in fact non-normal. The simulation data sets were generated with two test lengths (10 and 50 items) and four alternative true population distributions for the abilities: normal, bimodal, uniform, and chi-square. As expected, results showed that MML-Normal was the best method when the assumption of ability distribution was matched, regardless the test length. However, the accuracy or MML-Normal decreased with the violation level of the assumption of normal distribution of the latent ability. The MML-Discrete estimation could overcome well the weakness of the MML-Normal when the normality of the ability distribution was violated. The estimates of the corresponding standard errors produced by ACER ConQuest 3 were also being examined and discussed.

Citation Information
Luc T Le and Ray J Adams. "Accuracy of Rasch model item parameter estimation" (2013)
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