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.

Recent conceptualizations of psychopathy are moving toward more inclusive, purely trait-based models. However, researchers continue to heavily rely on assessments of psychopathy that include categorical behavioral elements. The newly developed Comprehensive Assessment of Psychopathic Personality – Institutional Rating Scale appears to be a promising interview-based measure of psychopathy, but research on its reliability and validity is in its infancy. As a second issue, the vast majority of research on psychopathy, particularly in offender populations, is conducted with male participants. Nonetheless, the growing body of literature involving incarcerated females suggests gender differences in the prevalence and manifestation of psychopathic traits. Reasons for these differences are unclear, but some have proposed socialized gender roles as a contributing factor. With a sample of 52 female 49 male offenders recruited from a large, metropolitan jail, this dissertation evaluated the construct validity of the CAPP-IRS and examined the effect of gender role conformity on the manifestation of psychopathic traits. Results indicated that a three-factor model of psychopathy represented by antagonistic interpersonal relations, restricted emotions, and disinhibited behavior best fit the data. Findings further suggested convergent and discriminant validity for the CAPP-IRS. Additionally, masculine and feminine gender role conformity differentially related to psychopathy, but generally accounted for a small proportion of the variance in psychopathic traits. Recommendations for future research on the CAPP model and its assessment as well as implications for the clinical assessment of psychopathy in women are discussed.

The intent of this thesis is to examine the psychometric properties of a complex span task (CST) developed to measure working memory capacity (WMC) using measurements obtained from a sample of 68 undergraduate students at the University of Central Florida. The Grocery List Task (GLT) promises several design improvements over traditional CSTs in a prior study about individual differences in WMC and distraction effects on driving performance, and it offers potential benefits for studying WMC as well as the serial-position effect. Currently, the working memory system is composed of domain-general memorial storage processes and information-processing, which involves the use of executive functions. Prior research has found WMC to be associated with attentional measures (i.e., executive attention) and the updating function, and unrelated to the shifting function. The present study replicates these relationships to other latent variables in measures obtained from the GLT as convergent and discriminant evidence of validity. In addition, GLT measures correlate strongly with established measures of WMC. Task reliability is assessed by estimates of internal consistency, pairwise comparisons with a cross-validation sample, and an analysis of demographic effects on task measurements.

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<p dir="ltr">It has been shown that formative assessments can lead to improvements in the learning process. Cognitive Diagnostic Models (CDMs) are a powerful formative assessment tool that can be used to provide individuals with valuable information regarding skill mastery in educational settings. These models provide each student with a ``skill mastery profile'' that shows the level of mastery they have obtained with regard to a specific set of skills. These profiles can be used to help both students and educators make more informed decisions regarding the educational process, which can in turn accelerate learning for students. However, despite their utility, these models are rarely used with small sample sizes. One reason for this is that these models are often complex, containing many parameters that can be difficult to estimate accurately when working with a small number of observations. This work aims to contribute to and expand upon previous work to make CDMs more accessible for a wider range of educators and students.</p><p dir="ltr">There are three main small sample statistical problems that we address in this work: 1) accurate estimation of the population distribution of skill mastery profiles, 2) accurate estimation of additional model parameters for CDMs as well as improved classification of individual skill mastery profiles, and 3) improved selection of an appropriate CDM for each item on the assessment. Each of these problems deals with a different aspect of educational measurement and the solutions provided to these problems can ultimately lead to improvements in the educational process for both students and teachers. By finding solutions to these problems that work well when using small sample sizes, we make it possible to improve learning in everyday classroom settings and not just in large scale assessment settings.</p><p dir="ltr">In the first part of this work, we propose novel algorithms for estimating the population distribution of skill mastery profiles for a popular CDM, the Deterministic Inputs Noisy ``and'' Gate (DINA) model. These algorithms borrow inspiration from the concepts behind popular machine learning algorithms. However, in contrast to these methods, which are often used solely for prediction, we illustrate how the ideas behind these methods can be adapted to obtain estimates of specific model parameters. Through studies involving simulated and real-life data, we illustrate how the proposed algorithms can be used to gain a better picture of the distribution of skill mastery profiles for an entire population students, but can do so by only using a small sample of students from that population. </p><p dir="ltr">In the second part of this work, we introduce a new method for regularizing high-dimensional CDMs using a class of Bayesian shrinkage priors known as catalytic priors. We show how a simpler model can first be fit to the observed data and then be used to generate additional pseudo-observations that, when combined with the original observations, make it easier to more accurately estimate the parameters in a complex model of interest. We propose an alternative, simpler model that can be used instead of the DINA model and show how the information from this model can be used to formulate an intuitive shrinkage prior that effectively regularizes model parameters. This makes it possible to improve the accuracy of parameter estimates for the more complex model, which in turn leads to better classification of skill mastery. We demonstrate the utility of this method in studies involving simulated and real-life data and show how the proposed approach is superior to other common approaches for small sample estimation of CDMs.</p><p dir="ltr">Finally, we discuss the important problem of selecting the most appropriate model for each item on assessment. Often, it is not uncommon in practice to use the same CDM for each item on an assessment. However, this can lead to suboptimal results in terms of parameter estimation and overall model fit. Current methods for item-level model selection rely on large sample asymptotic theory and are thus inappropriate when the sample size is small. We propose a Bayesian approach for performing item-level model selection using Reversible Jump Markov chain Monte Carlo. This approach allows for the simultaneous estimation of posterior probabilities and model parameters for each candidate model and does not require a large sample size to be valid. We again demonstrate through studies involving simulated and real-life data that the proposed approach leads to a much higher chance of selecting the best model for each item. This in turn leads to better estimates of item and other model parameters, which ultimately leads to more accurate information regarding skill mastery. </p>

No / This study sought to investigate the characteristics of 'nearby' leaders by eliciting the constructs of male and female top, senior, and middle-level managers and professionals working in organizations in two large UK public sectors (local government and the National Health Service). An instrument, the Transformational Leadership Questionnaire (TLQ-LGV), was developed and piloted on a national sample of 1464 managers working for local government organizations. Analysis of the data, presented here, revealed the existence of nine highly robust scales with high reliabilities (.85) and with convergent validity (range r = .46 to .85). These findings are discussed, together with suggestions for subsequent research.