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Description
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by proportions of replications in which the PPP-values were extreme. An effect size measure for PPMC was introduced as a supplemental numerical summary to the PPP-value. Consistent with previous PPMC research, all investigated fit functions tended to perform conservatively, but Standardized Generalized Dimensionality Discrepancy Measure (SGDDM), Yen's Q3, and Hierarchy Consistency Index (HCI) only mildly so. Adequate power to detect at least some types of misfit was demonstrated by SGDDM, Q3, HCI, Item Consistency Index (ICI), and to a lesser extent Deviance, while proportion correct (PC), a chi-square-type item-fit measure, Ranked Probability Score (RPS), and Good's Logarithmic Scale (GLS) were powerless across all investigated factors. Bivariate SGDDM and Q3 were found to provide powerful and detailed feedback for all investigated types of misfit.
ContributorsCrawford, Aaron (Author) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Thompson, Marilyn (Committee member) / Arizona State University (Publisher)
Created2014
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Description
The use of exams for classification purposes has become prevalent across many fields including professional assessment for employment screening and standards based testing in educational settings. Classification exams assign individuals to performance groups based on the comparison of their observed test scores to a pre-selected criterion (e.g. masters vs. nonmasters

The use of exams for classification purposes has become prevalent across many fields including professional assessment for employment screening and standards based testing in educational settings. Classification exams assign individuals to performance groups based on the comparison of their observed test scores to a pre-selected criterion (e.g. masters vs. nonmasters in dichotomous classification scenarios). The successful use of exams for classification purposes assumes at least minimal levels of accuracy of these classifications. Classification accuracy is an index that reflects the rate of correct classification of individuals into the same category which contains their true ability score. Traditional methods estimate classification accuracy via methods which assume that true scores follow a four-parameter beta-binomial distribution. Recent research suggests that Item Response Theory may be a preferable alternative framework for estimating examinees' true scores and may return more accurate classifications based on these scores. Researchers hypothesized that test length, the location of the cut score, the distribution of items, and the distribution of examinee ability would impact the recovery of accurate estimates of classification accuracy. The current simulation study manipulated these factors to assess their potential influence on classification accuracy. Observed classification as masters vs. nonmasters, true classification accuracy, estimated classification accuracy, BIAS, and RMSE were analyzed. In addition, Analysis of Variance tests were conducted to determine whether an interrelationship existed between levels of the four manipulated factors. Results showed small values of estimated classification accuracy and increased BIAS in accuracy estimates with few items, mismatched distributions of item difficulty and examinee ability, and extreme cut scores. A significant four-way interaction between manipulated variables was observed. In additional to interpretations of these findings and explanation of potential causes for the recovered values, recommendations that inform practice and avenues of future research are provided.
ContributorsKunze, Katie (Author) / Gorin, Joanna (Thesis advisor) / Levy, Roy (Thesis advisor) / Green, Samuel (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Research suggests that some children with primary language impairment (PLI)

have difficulty with certain aspects of executive function; however, most studies examining executive function have been conducted using tasks that require children to use language to complete the task. As a result, it is unclear whether poor performance on executive function

Research suggests that some children with primary language impairment (PLI)

have difficulty with certain aspects of executive function; however, most studies examining executive function have been conducted using tasks that require children to use language to complete the task. As a result, it is unclear whether poor performance on executive function tasks was due to language impairment, to executive function deficits, or both. The purpose of this study is to evaluate whether preschoolers with PLI have deficits in executive function by comprehensively examining inhibition, updating, and mental set shifting using tasks that do and do not required language to complete the tasks.

Twenty-two four and five-year-old preschoolers with PLI and 30 age-matched preschoolers with typical development (TD) completed two sets of computerized executive function tasks that measured inhibition, updating, and mental set shifting. The first set of tasks were language based and the second were visually-based. This permitted us to test the hypothesis that poor performance on executive function tasks results from poor executive function rather than language impairment. A series of one-way analyses of covariance (ANCOVAs) were completed to test whether there was a significant between-group difference on each task after controlling for attention scale scores. In each analysis the between-group factor was group and the covariate was attention scale scores.

Results showed that preschoolers with PLI showed difficulties on a broad range of linguistic and visual executive function tasks even with scores on an attention measure covaried. Executive function deficits were found for linguistic inhibition, linguistic and visual updating, and linguistic and visual mental set shifting. Overall, findings add to evidence showing that the executive functioning deficits of children with PLI is not limited to the language domain, but is more general in nature. Implications for early assessment and intervention will be discussed.
ContributorsYang, Huichun (Author) / Gray, Shelley (Thesis advisor) / Restrepo, Maria (Committee member) / Azuma, Tamiko (Committee member) / Green, Samuel (Committee member) / Arizona State University (Publisher)
Created2015