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.
Le, L. T., & Adams, R. J. (2013). Accuracy of Rasch model item parameter estimation. https://research.acer.edu.au/ar_misc/13