Matching Items (2)
Filtering by
- Member of: Theses and Dissertations
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 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
… types of misfit. Date Created 2014 Contributors Crawford, Aaron (Author) Levy, Roy (Thesis advisor) Green, Samuel … Statement of Responsibility by AAron Crawford Description Source Viewed on May 6, 2015 … Posterior Predictive Model Checking in Bayesian Networks by AAron Crawford A Dissertation Presented in Partial …
Description
ABSTRACT Perfectionism has been conceptualized as a relatively stable, independent, multidimensional personality construct in research during the last two decades. Despite general agreement that perfectionism is dimensional in nature, analyses using these instruments vacillate between a dimensional approach and a categorical approach (Broman-Fulks, Hill, & Green, 2008; Stoeber & Otto, 2006). The goal of the current study was two-fold. One aim was to examine the structural nature of two commonly used measures of perfectionism, the APS-R and the HFMPS. Latent class and factor analyses were conducted to determine the dimensions and categories that underlie the items of these two instruments. A second aim was to determine whether perfectionism classes or perfectionism factors better predicted 4 criterion variables of career indecision. Results lent evidence to the claim that both the APS-R and HFMPS are best used as dimensional, rather than categorical instruments. From a substantive perspective, results indicated that both positive and negative aspects of perfectionism successfully predicted career indecision factors. The study concludes with a discussion of limitations, and implications for future research and counseling individuals with career indecision concerns.
ContributorsRohlfing, Jessica Elizabeth (Author) / Tracey, Terence J. G. (Thesis advisor) / Green, Samuel (Committee member) / Kinnier, Richard T. (Committee member) / Arizona State University (Publisher)
Created2013
… & R.R. Ansbacher (Eds.), The individual psychology of Alfred Adler. New York: Harper. Ashby, J. S., & Bruner, L. …