SEM of another flavour: two new applications of the supplemented EM algorithm.
The supplemented EM (SEM) algorithm is applied to address two goodness-of-fit testing problems in psychometrics. The first problem involves computing the information matrix for item parameters in item response theory models. This matrix is important for limited-information goodness-of-fit testing and it is also used to compute standard errors for the item parameter estimates. For the second problem, it is shown that the SEM algorithm provides a convenient computational procedure that leads to an asymptotically chi-squared goodness-of-fit statistic for the 'two-stage EM' procedure of fitting covariance structure models in the presence of missing data. Both simulated and real data are used to illustrate the proposed procedures.
Cai
Li
L
Graduate School of Education and Information Studies, UCLA, Los Angeles, CA 90095-1521, USA. lcai@ucla.edu
D000465:Algorithms; D003627:Data Interpretation, Statistical; D006801:Humans; D008960:Models, Psychological