title: Estimation of IRT graded response models: limited versus full information methods.
abstract: The performance of parameter estimates and standard errors in estimating F. Samejima's graded response model was examined across 324 conditions. Full information maximum likelihood (FIML) was compared with a 3-stage estimator for categorical item factor analysis (CIFA) when the unweighted least squares method was used in CIFA's third stage. CIFA is much faster in estimating multidimensional models, particularly with correlated dimensions. Overall, CIFA yields slightly more accurate parameter estimates, and FIML yields slightly more accurate standard errors. Yet, across most conditions, differences between methods are negligible. FIML is the best election in small sample sizes (200 observations). CIFA is the best election in larger samples (on computational grounds). Both methods failed in a number of conditions, most of which involved 200 observations, few indicators per dimension, highly skewed items, or low factor loadings. These conditions are to be avoided in applications.
authors.affiliation: Department of Personality, Evaluation and Psychological Treatment, Faculty of Psychology, University of Barcelona, Spain. carlos.garcia@ub.edu
authors.forename: Carlos G
authors.initials: CG
authors.lastname: Forero
authors.forename: Alberto
authors.initials: A
authors.lastname: Maydeu-Olivares
mesh_terms: D005163:Factor Analysis, Statistical; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D016011:Normal Distribution; D011581:Psychological Tests / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results