期刊名称：International Journal of Assessment Tools in Education
电子版ISSN：2148-7456
出版年度：2019
卷号：6
期号：1
页码：92-108
DOI：10.21449/ijate.523586
语种：English
出版社：International Journal of Assessment Tools in Education
摘要：Applying single-level statistical models to multilevel data typically produces underestimated standard errors,which may result in misleading conclusions.This study examined the impact of ignoring multilevel data structure on the estimation of item parameters and their standard errors of the Rasch,two-,and threeparameter logistic models in item response theory (IRT) to demonstrate the degree of such underestimation in IRT.Also,the Lord’s chi-square test using the underestimated standard errors was used to test differential item functioning (DIF) to show the impact of such underestimation on the practical applications of IRT.The results of simulation studies showed that,in the most severe case of multilevel data,the standard error estimate from the standard singlelevel IRT models was about half of the minimal asymptotic standard error,and the type I error rate of the Lord’s chi-square test was inflated up to.35.The results of this study suggest that standard single-level IRT models may seriously mislead our conclusions in the presence of multilevel data,and therefore multilevel IRT models need to be considered as alternatives.
关键词：Item Response Theory;Rasch;Multilevel Data;Monte Carlo Simulation
