title: Comparability of item quality indices  from sparse data matrices with random and non-random missing data patterns.
abstract: This article summarizes a simulation study of the performance of five item quality indicators (the weighted and unweighted versions of the mean square and standardized mean square fit indices and the point-measure correlation) under conditions of relatively high and low amounts of missing data under both random and conditional patterns of missing data for testing contexts such as those encountered in operational administrations of a computerized adaptive certification or licensure examination. The results suggest that weighted fit indices, particularly the standardized mean square index, and the point-measure correlation provide the most consistent information between random and conditional missing data patterns and that these indices perform more comparably for items near the passing score than for items with extreme difficulty values.
authors.affiliation: Pearson, Mailstop 125, 2510 N. Dodge St., Iowa City, IA 52245-9945, USA. ed.wolfe@pearson.com
authors.forename: Edward W
authors.initials: EW
authors.lastname: Wolfe
authors.forename: Michael T
authors.initials: MT
authors.lastname: McGill
mesh_terms: D015982:Bias; D003196:Computer Graphics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008008:Licensure, Nursing / Q000706:statistics & numerical data*; D016014:Linear Models; D008432:Mathematical Computing; D015203:Reproducibility of Results; D018401:Sample Size; D012984:Software