title: Estimation of decision consistency indices for complex assessments: model based approaches.
abstract: With the implementation of the No Child Left Behind assessment program and the use of proficiency levels as a means of evaluating Annual Yearly Progress, there is a renewed interest in the consistency of classification decisions based on scale scores from achievement test and state-wide proficiency standards. Many of the current methods described in the literature (Huynh, 1976; Hanson and Brennan, 1990; and Livingston and Lewis, 1995) are based on assumptions about the distribution of the conditional errors. Although recent methods (Brennan and Wan, 2004) make no assumptions about the distribution, these methods have one compelling disadvantage: the decision consistency calculated is based on the entire set of data and are not conditional on the location of the cut scores, the student measure and the conditional standard errors of measurement for the students. The decision consistency for a student scoring right at the cut score will be much lower that the decision consistency for a student with a score 5 points above or below that cut score. The standard error method described in this article is based solely on the asymptotic standard error of measurement derived from the appropriate Rasch measurement model, and the location of the cut score used to make the classification decision. This classification can be easily modified to accommodate multiple classification categories. This is a conditional decision consistency statistic that can be applied to each person ability estimate (raw score) and provides information that can be used to calculate the likelihood that a person with that measure will receive the same classification if retested. The decision consistency for the entire sample can be calculated by simply summing the likelihood of the same classification over all of the examinees. The results of retest simulations using data that fit the Rasch model suggest that the standard error method provides a better estimate of the resulting classification consistency than the true score methods or the bootstrap method.
authors.affiliation: Psychometric Services, Data Recognition Corporation, 13490 Bass Lake Road, Maple Grove, MN 55311, USA. mstearns@datarecognitioncorp.com
authors.forename: Matthew
authors.initials: M
authors.lastname: Stearns
authors.forename: Richard M
authors.initials: RM
authors.lastname: Smith