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The Outcome Questionnaire-45 (OQ-45) is a 45-item instrument designed to be used by psychotherapists to track their clients' distress over time. The OQ-45 is composed of three factors: symptomatic distress, interpersonal relations, and social role performance. Numerous researchers have attempted to replicate this intended three-factor structure in their own data, only to find poor fit. Attempts to find a factor structure that does show adequate fit have been met with mixed, but generally poor, results. Additionally, very little work has been done to establish that the OQ-45 exhibits sufficient longitudinal measurement invariance to allow comparison of OQ-45 scores over time. Notwithstanding these known issues regarding the fit of the OQ-45, it has been adopted widely in many countries and translated into several dozen languages. This study sought to identify a factor structure of the OQ-45 that did exhibit longitudinal measurement invariance. Using a sample of 7,751 clients who made 56,353 visits to Brigham Young University's Counseling and Psychological Services between 1996 and 2017, three factor structures were analyzed using Mplus 8.2 through confirmatory factor analysis: (a) single-factor, (b) intended three-factor, and (c) bifactor models. The bifactor model fit the data best, as determined by standard fit statistics (CFI, TLI, RMSEA, SRMR). However, this bifactor model still had inadequate fit. At this point, exploratory structural equation modeling (ESEM) using target rotation was applied to the bifactor model. This ESEM bifactor model had a dominant general factor and did have good fit to the data. Having selected the ESEM bifactor model, it was then tested to see if it showed longitudinal measurement invariance over five time points (the initial OQ-45 score at the intake appointment, followed by four subsequent appointments). The OQ-45 items were treated as categorical and analyzed using the WLSMV estimator. Four time sequences were examined for configural, metric, and scalar longitudinal invariance: Time 1 to Time 2, Time 1 to Time 3 (inclusive of Time 2), Time 1 to Time 4 (inclusive of Times 2 and 3), and Time 1 to Time 5 (inclusive of Times 2, 3, and 4). The OQ-45, when modeled as an ESEM bifactor model, does exhibit scalar longitudinal measurement invariance. Using a new method developed by Clark (2020), ΔSRMR between adjacent models (configural to metric, metric to scalar) were all below his recommended guideline of .01. This is the first study to find a good fitting model of the OQ- 45 that can be used to assess changes in clients' psychological functioning over time. Total OQ- 45 scores can continue to be used by therapists to monitor their patients with confidence in its longitudinal psychometric properties.

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Asians consistently report higher social anxiety symptoms but have lower prevalence rates, compared to Westerners. As cultural differences and measurement issues could both be potential sources for the discrepancy, it is important to examine whether score differences between cultural groups are due to measurement issues or genuine underlying differences in social anxiety. This study used 402 participants to examine the construct of social anxiety and measurement invariance of six social anxiety scales using exploratory and confirmatory factor analysis. Results supported scalar invariance of a three-factor bifactor model (comprised of Fear/Avoidance of Social Interaction, Fear of Negative Evaluation, and Taijin Kyofusho/fear of interpersonal relationships). Furthermore, multivariate analysis of covariance and moderation analysis revealed Asian Americans endorsed higher Fear/Avoidance of Social Interaction symptoms, but do not differ in Taijin Kyofusho and Fear of Negative Evaluation symptoms, compared to European Americans. This study showed when measurement bias is minimized, Asians still endorse higher symptoms of Fear/Avoidance of Social Interactions. Moreover, Taijin Kyofusho appears to be an aspect of social anxiety identified in more than one cultural group rather than a culture-related specific syndrome. Thus, it is important for clinicians and researchers to consider Taijin Kyofusho in the evaluation of social anxiety. / School Psychology

Previous research has been limited regarding the measurement of computational thinking, particularly as a learning progression in K-12. This study proposes to apply a multidimensional item response theory (IRT) model to a newly developed measure of computational thinking utilizing both selected response and open-ended polytomous items to establish the factorial structure of the construct, apply the recently introduced composite and structured constructs models, and to investigate the measurement invariance of the assessment between males and females using the means and covariance structures (MACS) approach.

Innovative people seize the opportunity to make lives better and more comfortable, which contribute to economy growth and financial gain. Stakeholders study innovativeness of business students, in depth, to understand gender differences, and the factors affecting students' innovativeness. Literature explains how males and females differ in their proneness to guilt and shame. However, a model that explains the dynamic of guilt, shame, and gender on innovativeness will help make policies to improve students' innovativeness. This study describes factor analysis approach to examine the TOSCA-3 subscales guilt, shame, and the DNA instrument of innovativeness. It also describes the measurement invariance across gender for each construct, and for the full measurement model to identify the differences between genders. Moreover, this study examines the total effect of gender on innovativeness, which includes the direct effect, and indirect effect via guilt and shame. The results indicated that guilt is positively associated with innovativeness, and shame and gender are negatively associated with innovativeness. This dissertation can be freely accessed and downloaded from (http://etd.byu.edu/).

The Study Anxiety Inventory (SAI), consisting of the factors of worry and emotionality, was developed to measure college students' self-reported levels of anxiety while studying for an exam. Data from 2002 undergraduate students from four colleges (Arts and Sciences, Engineering, Business, and Education) at a southeastern state university were used to evaluate the validity of the scores from the 16-item Study Anxiety Inventory. Results of confirmatory factor analyses for the two factor model, conducted separately for each college, indicated marginally acceptable fit for the data (median fit measures across the four colleges: CFI =.915, SRMR=.049, RMSEA=.098), a pattern that was repeated for both males and females. Multigroup CFA was used to evaluate the factorial invariance of the SAI across gender within each college. Factor loadings (i.e., pattern coefficients) for the SAI items were not found to be significantly different between males and females (p > .05). Error variances for four items were found to be significantly different between males and females, indicating that there may be some difference in scale reliability by gender. Factor covariances were invariant for all four colleges (p > .05) and factor variances were invariant for all but the worry component for the College of Arts and Sciences where females had significantly greater variability on the worry factor. As was hypothesized, the SAI scores were positively correlated with scores on measures of test anxiety (median r=.74), trait anxiety (median r=.46), active procrastination (median r=.23), and passive procrastination (median r=.29), but negatively correlated with trait curiosity (median r=-.19). Contrary to what was hypothesized, no relationship was demonstrated between study anxiety and study skills and habits (median r=-.03). The nomological network was extended in this study by examining relationships between scores obtained from students on the SAI and measures of active and passive procrastination. This is the first study that systematically examines the factorial invariance of the SAI by gender, which is important because previous research using the SAI has shown men's scores to be consistently lower than women's scores. The results obtained in the current study provide support for gender invariance in a nonclinical population in the situation specific level of anxiety while studying. There is sufficient evidence of validity and reliability (median Cronbach alphas for males and females for the total score were .978 and .980, for worry were .968 and .973, and for emotionality were .947 and .951, respectively) that a researcher should feel confident that the SAI is a psychometrically sound research tool that holds up fairly well across a number of different types of students and that making mean comparisons on the SAI by gender is acceptable.

abstract: In the past, it has been assumed that measurement and predictive invariance are consistent so that if one form of invariance holds the other form should also hold. However, some studies have proven that both forms of invariance only hold under certain conditions such as factorial invariance and invariance in the common factor variances. The present research examined Type I errors and the statistical power of a method that detects violations to the factorial invariant model in the presence of group differences in regression intercepts, under different sample sizes and different number of predictors (one or two). Data were simulated under two models: in model A only differences in the factor means were allowed, while model B violated invariance. A factorial invariant model was fitted to the data. Type I errors were defined as the proportion of samples in which the hypothesis of invariance was incorrectly rejected, and statistical power was defined as the proportion of samples in which the hypothesis of factorial invariance was correctly rejected. In the case of one predictor, the results show that the chi-square statistic has low power to detect violations to the model. Unexpected and systematic results were obtained regarding the negative unique variance in the predictor. It is proposed that negative unique variance in the predictor can be used as indication of measurement bias instead of the chi-square fit statistic with sample sizes of 500 or more. The results of the two predictor case show larger power. In both cases Type I errors were as expected. The implications of the results and some suggestions for increasing the power of the method are provided. / Dissertation/Thesis / M.A. Psychology 2010

abstract: Researchers who conduct longitudinal studies are inherently interested in studying individual and population changes over time (e.g., mathematics achievement, subjective well-being). To answer such research questions, models of change (e.g., growth models) make the assumption of longitudinal measurement invariance. In many applied situations, key constructs are measured by a collection of ordered-categorical indicators (e.g., Likert scale items). To evaluate longitudinal measurement invariance with ordered-categorical indicators, a set of hierarchical models can be sequentially tested and compared. If the statistical tests of measurement invariance fail to be supported for one of the models, it is useful to have a method with which to gauge the practical significance of the differences in measurement model parameters over time. Drawing on studies of latent growth models and second-order latent growth models with continuous indicators (e.g., Kim & Willson, 2014a; 2014b; Leite, 2007; Wirth, 2008), this study examined the performance of a potential sensitivity analysis to gauge the practical significance of violations of longitudinal measurement invariance for ordered-categorical indicators using second-order latent growth models. The change in the estimate of the second-order growth parameters following the addition of an incorrect level of measurement invariance constraints at the first-order level was used as an effect size for measurement non-invariance. This study investigated how sensitive the proposed sensitivity analysis was to different locations of non-invariance (i.e., non-invariance in the factor loadings, the thresholds, and the unique factor variances) given a sufficient sample size. This study also examined whether the sensitivity of the proposed sensitivity analysis depended on a number of other factors including the magnitude of non-invariance, the number of non-invariant indicators, the number of non-invariant occasions, and the number of response categories in the indicators. / Dissertation/Thesis / Doctoral Dissertation Psychology 2016

abstract: Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses. This study explored the effects of mismatch in dimensionality between data and analysis models with multiple-group analyses at the population and sample levels. Datasets were generated using a bifactor model with different factor structures and were analyzed with bifactor and single-factor models to assess misspecification effects on assessments of MI and latent mean differences. As baseline models, the bifactor models fit data well and had minimal bias in latent mean estimation. However, the low convergence rates of fitting bifactor models to data with complex structures and small sample sizes caused concern. On the other hand, effects of fitting the misspecified single-factor models on the assessments of MI and latent means differed by the bifactor structures underlying data. For data following one general factor and one group factor affecting a small set of indicators, the effects of ignoring the group factor in analysis models on the tests of MI and latent mean differences were mild. In contrast, for data following one general factor and several group factors, oversimplifications of analysis models can lead to inaccurate conclusions regarding MI assessment and latent mean estimation. / Dissertation/Thesis / Doctoral Dissertation Educational Psychology 2018