The current study examined the psychometric properties of the Pathways to Inflated Responsibility Beliefs Scale (PIRBS; Coles & Schofield, 2008), a measure designed to assess the theoretical pathways posited to contribute to responsibility beliefs in individuals with obsessive-compulsive disorder (OCD; Salkovskis et al., 1999). The primary aim of this study was to examine the factor structure, reliability, and validity of the measure in a diverse sample as well as to compare the properties of the scale across ethnic groups. The current sample consisted of 442 university students who completed questionnaire packets or an online survey. The results of an exploratory factor analysis suggested that a four-factor model with three items removed from the original PIRBS scale best fit the data. Confirmatory factor analyses in groups of African American and Caucasian participants indicated that neither the original PIRBS model nor the EFA-derived model adequately fit the data, but the latter model demonstrated comparable indicators of validity as well as an improvement in the internal consistency of the PIRBS Overprotection subscale. Evidence of the convergent and discriminant validity of the PIRBS was obtained through its associations with OCD-relevant constructs, including OC beliefs domains and symptoms, trait anxiety and worry, depression, parenting styles, a measure of childhood responsibility, and religiosity. Some differential associations were observed in these relationships across ethnic groups. Suggestions for future research and the clinical implications of research in this area are discussed.

This study conducted a series of road tests in the regular fleet operations of a revenue service to better understand the relationship between vehicle seat design and driver fatigue, improve two newly proposed objective methods for evaluating driver fatigue, and provide design guidelines for evaluating and improving vehicle seat characteristics in terms of driver fatigue. Each driver completed a test session on two seat cushions - one a polyurethane foam cushion and one an air-inflated cushion. Objective measurements of pressure distribution were taken throughout each test session, while subjective measurements were collected using surveys taken at one-hour intervals. Based on these results, we find that the air-inflated seat cushion has advantages in terms of subjective measures of comfort, support, and fatigue. We show that the objective measure aPcrms highlights characteristic differences between seat cushions, as the air-inflated seat cushion provides less area in high pressure regions, thus occluding less blood flow to tissue in the seated area. While we were unable to effectively assess the validity of the proposed measures or improve them further, the characteristic difference between seat cushions is not highlighted by using previously existing objective measures. This implies that aPcrms is a more useful measure and should be considered when evaluating the subjective quality of seat cushion designs under dynamic conditions, such as those existing in commercial truck driving. / Master of Science

Doctor of Philosophy / Department of Statistics / Wei-Wen Hsu / Evaluating heterogeneity in the class of zero-inflated models has attracted considerable attention in the literature, where the heterogeneity refers to the instances of zero counts generated from two different sources. The mixture probability or the so-called mixing weight in the zero-inflated model is used to measure the extent of such heterogeneity in the population. Typically, the homogeneity tests are employed to examine the mixing weight at zero. Various testing procedures for homogeneity in zero-inflated models, such as score test and Wald test, have been well discussed and established in the literature. However, it is well known that these classical tests require the correct model specification in order to provide valid statistical inferences. In practice, the testing procedure could be performed under model misspecification, which could result in biased and invalid inferences. There are two common misspecifications in zero-inflated models, which are the incorrect specification of the baseline distribution and the misspecified mean function of the baseline distribution. As an empirical evidence, intensive simulation studies revealed that the empirical sizes of the homogeneity tests for zero-inflated models might behave extremely liberal and unstable under these misspecifications for both cross-sectional and correlated count data. We propose a robust score statistic to evaluate heterogeneity in cross-sectional zero-inflated data. Technically, the test is developed based on the Poisson-Gamma mixture model which provides a more general framework to incorporate various baseline distributions without specifying their associated mean function. The testing procedure is further extended to correlated count data. We develop a robust Wald test statistic for correlated count data with the use of working independence model assumption coupled with a sandwich estimator to adjust for any misspecification of the covariance structure in the data. The empirical performances of the proposed robust score test and Wald test are evaluated in simulation studies. It is worth to mention that the proposed Wald test can be implemented easily with minimal programming efforts in a routine statistical software such as SAS. Dental caries data from the Detroit Dental Health Project (DDHP) and Girl Scout data from Scouting Nutrition and Activity Program (SNAP) are used to illustrate the proposed methodologies.