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

College student satisfaction is a concept that has become more prevalent in higher education research journals. Little attention has been given to the psychometric properties of previous instrumentation, and few studies have investigated the structure of current satisfaction instrumentation. This dissertation: (a) investigated the tenability of the theoretical dimensional structure of the Noel-Levitz Student Satisfaction Inventory™ (SSI), (b) investigated an alternative factor structure using explanatory factor analyses (EFA), and (c) used multiple-group CFA procedures to determine whether an alternative SSI factor structure would be invariant for three demographic variables: gender (men/women), race/ethnicity (Caucasian/Other), and undergraduate classification level (lower level/upper level). For this study, there was little evidence for the multidimensional structure of the SSI. A single factor, termed General Satisfaction with College, was the lone unidimensional construct that emerged from the iterative CFA and EFA procedures. A revised 20-item model was developed, and a series of multigroup CFAs were used to detect measurement invariance for three variables: student gender, race/ethnicity, and class level. No measurement invariance was noted for the revised 20-item model. Results for the invariance tests indicated equivalence across the comparison groups for (a) the number of factors, (b) the pattern of indicator-factor loadings, (c) the factor loadings, and (d) the item error variances. Because little attention has been given to the psychometric properties of the satisfaction instrumentation, it is recommended that further research continue on the SSI and any additional instrumentation developed to measure student satisfaction. It is possible that invariance issues may explain a portion of the inconsistent findings noted in the review of literature. Although measurement analyses are a time-consuming process, they are essential for understanding the psychometrics characterized by a set of scores obtained from a survey, or any other form of assessment instrument.

The common practice for testing measurement invariance is to constrain parameters to be equal over groups, and then evaluate the model-data fit to reject or fail to reject the restrictive model. Posterior predictive checking (PPC) provides an alternative approach to evaluating model-data discrepancy. This paper explores the utility of PPC in estimating measurement invariance. The simulation results show that the posterior predictive p (PP p) values of item parameter estimates respond to various invariance violations, whereas the PP p values of item-fit index may fail to detect such violations. The current paper suggests comparing group estimates and restrictive model estimates with posterior predictive distributions in order to demonstrate the pattern of misfit graphically.

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Modern theories of authoritarianism have stressed the importance of threat to the expression of authoritarian attitudes and intolerance. Arguably, authoritarian tendencies may have increased during COVID-19 pandemic, a major threat to life and security. One issue arising when comparing mean scores is that of measurement invariance. Meaningful comparisons are only possible, if latent constructs are similar between groups and/or across time. This prerequisite is rarely ever tested in research on authoritarianism. In this study, we aim to analyze the short scale for authoritarianism KSA-3 by investigating its measurement invariance on two levels (three first-order and one second-order factors) and latent mean changes using two German representative samples (N = 4,905). Specifically, we look at differences before and during the pandemic (2017 vs. 2020). While measurement invariance holds across both levels in all conditions, we find a decrease in latent means in 2020, contrary to expectations and established theories. Moreover, latent means differ with regard to gender, education, and east–west Germany. We conclude that analyses of latent means and measurement invariance instead of mean comparisons with composites should become the standard. Future studies should focus on threat as a moderator between authoritarianism and intolerance, and on possible interactions with context variables.

Family functioning has long been a focus of research in psychopathology. Decades of research has shown that family factors are associated with symptom severity, relapse, functional outcomes, and conversion to psychosis among at-risk individuals. Previous studies suggest family functioning varies across cultures, which raises the possibility that associations between family factors and psychopathology may also differ by culture. Furthermore, family functioning assessment generally involves instruments that have not been systematically validated for use with individuals from diverse cultural backgrounds. The current study used data from the Adolescent Brain Cognitive Development study (N = 11,138) to: (1) evaluate three family functioning scales (i.e., Family Environment Scale, Child's Report of Parental Behavior Inventory, Parental Monitoring Survey) and the Prodromal Questionnaire – Brief Child version for measurement invariance across racial/ethnic groups; (2) investigate the relations between family factors and psychosis; and (3) compare relations derived from Step 2 between racial/ethnic groups. Full scalar invariance was tenable for the CRPBI and the PQ-BC, providing statistical support for mean comparisons across groups. The FES and the PMQ lacked scalar invariance, which suggests mean comparisons across groups may not be appropriate. The CRPBI and the PMQ are significantly associated with the PQ-BC, and all three family scales had equivalent relations with the PQ-BC across groups. The current study highlights the importance of evaluating assessment instruments for measurement invariance across racial/ethnic groups. Results also help to connect specific family factors to the etiology of psychotic disorders among US children and adolescents.

<p>This dissertation examined several dimensions of the development of physical aggression and indirect aggression in a longitudinal sample of boys and girls. These data are part of the National Longitudinal Study of Children and Youth which evaluated the development of children bi-annually from 1994 to 2010. The data for this thesis come from individuals aged 10 and 11 in Cycle 1 (1994) through to Cycle 5 (2002) when they were 18 and 19. In an attempt to explore trends in the development of aggression, the research is presented as three separate projects that examine the following: (1) measurement of physical and indirect aggression by informant and sex; (2) group-based trajectories of physical and indirect aggression and outcomes of trajectories in emerging adulthood; and (3) association between indirect aggression in adolescence and depression in emerging adulthood when physical aggression is taken into account. This is the first longitudinal study to investigate group-based physical and indirect aggression trajectories in childhood and adolescence and outcomes in emerging adulthood. The contribution of this thesis to the field of epidemiological research on aggression is the importance of considering distinct subgroups within both physical and indirect aggression, and joint trajectory groups of both physical and indirect aggression when exploring developmental trends and outcomes of aggression.</p> / Doctor of Philosophy (PhD)

Cross-cultural research requires tests of measurement invariance to determine if different populations have equivalent latent constructs. A psychometric assessment of the Agricultural Production Perspectives Scale (APPS) was necessary to determine the validity of the scale constructed and whether data from multiple countries could be compared. Using 918 farmers sampled from Kenya, Uganda, Mali, and Lesotho, I conducted exploratory factor analysis and scale reliability tests to determine whether the item loadings and factors were equivalent across populations. No factor structure could be obtained across country or agroecological populations. The data were reanalyzed within each agroecology to determine localized factor structures. Results indicate that a market driven factor and agrarian driven factor tend to emerge across multiple agroecologies suggesting some emergence of latent variables. Recommendations for scale revisions are included to increase reliability and measurement invariance. / Master of Science / CCRA-8 (Technology Networks for Sustainable Innovation)