One of the nation's most pressing health related issues is that of healthy diet and proper nutrition. Because much research has shown that many Americans are in poor health or are at risk to become so due to poor diet and nutrition, understanding the psychological factors of a healthy diet or lack thereof is of the utmost importance. In order to understand the adoption and maintenance of health related behaviors, the link between intentions and behaviors must be evaluated. Of current health behavior models, the model utilized in this study was the Health Action Process Approach model (HAPA), which addressed this "intention-behavior gap." The HAPA model proposes that planning is the key mediator of the link between intentions and behavior. The current research was performed in two stages. The first stage evaluated the psychosocial constructs of the HAPA model, and their predictive utility for current diet and the second stage evaluated a planning-based intervention that aimed to increase proper nutrition in college-aged women. All HAPA constructs were found to be significantly correlated with one another, and planning was found to significantly and fully mediate the link between intention and healthy diet. The intervention did not lead to an increase in healthy diet relative to a standard-of-care control, although all participants across conditions reported increased intention, self-efficacy, and healthy diet from pre-test to follow-up.

Evidence thus far has not lent credence to facilitate lie detection by the average person. According to studies, there are five major signs of lying: lip pursing, narrowed eyebrows, shoulder shrugs, looking to the left, and smirking. The present study aims to determine whether training people in detecting the five signs of lying will facilitate lie detection in the average person. We analyzed the accuracy of lie detection by examining the verdicts of 155 undergraduate students during simulated police interrogations. Comparisons between the trained and untrained subjects support the hypothesis that the average person is no better than chance at detecting lies through non-verbal cues.

Purpose: Over-identification of Navajo Head Start children into special education on the Navajo Reservation has come to the attention of Tribal leaders, Educational leaders, and parents due to the use of invalid assessment measures. Dynamic assessment (DA) of narratives may be a tool for distinguishing language differences from language disorders. The purpose of this study is to determine whether the Predictive Early Assessment of Reading and Language (PEARL), a dynamic assessment of narratives, accurately classifies Navajo Head Start students with typically developing (TD) language or with language impairment (LI), and to examine which measures best predict children’s overall performances on the PEARL.

Method: Ninety, 4- and 5-year-old Navajo preschoolers with LI and with TD language were selected. Children completed the PEARL, which measured both language comprehension and production using pretest and posttest scores, and a modifiability scale. In addition, children completed the Clinical Evaluation of Language Fundamental, Preschool, Second Edition (CELF – Preschool 2) and language samples. A Navajo Speech Language Pathologist confirmed the diagnosis of the participants. Research assistants pretested, briefly taught the principles of narrative structure (story grammar, language complexity and episode) and evaluated response to learning using an index of modifiability.

Results: Results of discriminant analysis indicated that PEARL pretest differentiated both ability groups with 89% accuracy. In addition, posttest scores discriminated with 89% accuracy and modifiability scores with 100% accuracy. Further, the subtest story grammar was the best predictor at pretest and posttest, although modifiability scores were better predictors of both ability groups.

Conclusion: Findings indicate that the PEARL is a promising assessment for accurately differentiating Navajo preschool children with LI from Navajo preschool children with TD language. The PEARL’s recommended pretest cut score over-identified Navajo children with TD language; therefore, a new recommended cut score was determined.

This paper investigates a relatively new analysis method for longitudinal data in the framework of functional data analysis. This approach treats longitudinal data as so-called sparse functional data. The first section of the paper introduces functional data and the general ideas of functional data analysis. The second section discusses the analysis of longitudinal data in the context of functional data analysis, while considering the unique characteristics of longitudinal data such, in particular sparseness and missing data. The third section introduces functional mixed-effects models that can handle these unique characteristics of sparseness and missingness. The next section discusses a preliminary simulation study conducted to examine the performance of a functional mixed-effects model under various conditions. An extended simulation study was carried out to evaluate the estimation accuracy of a functional mixed-effects model. Specifically, the accuracy of the estimated trajectories was examined under various conditions including different types of missing data and varying levels of sparseness.

Given the major investment young people make in earning and maintaining a peer reputation, our goal in this study was to explore the association between dimensions of negative and positive peer reputation in middle school and adjustment several years later, by the end of high school, among upper middle class youth. Prior research has shown negative reputations such as aggressive-disruptive and sensitive-isolated to be associated with maladjustment later in life, whereas reputations like popular and prosocial-leader have been related to positive future outcomes. However, there are contrary findings that reveal a more complex relationship between peer reputation and adjustment, showing certain “negative” reputations to be tied with better outcomes in some domains and the converse in others. Using a sample of middle school students, a confirmatory factor analysis (CFA) was performed to test a four-factor model of the Revised Class Play, a peer report measure on peer reputations. CFA findings supported the four-factor model with the following reputations: popular, prosocial, aggressive, and isolated. Structural equation models were used to predict 12th grade adjustment outcomes (academic achievement, psychopathology, substance use) from middle school peer reputation. Prosocial reputation in middle school was connected to higher academic achievement and fewer externalizing symptoms in 12th grade. Both prosocial and isolated peer reputation were negatively associated with alcohol, cigarette, and marijuana use, whereas a popular reputation was related to higher levels of alcohol use. Middle school reputation did not predict internalizing symptoms in 12th grade. Findings are discussed in terms of adaptive and maladaptive adjustment outcomes associated with each peer reputation and implications for future research.

The present study tested the respective mediating effects of sensation seeking and initial level of response (LR) to negative, sedative alcohol effects on the relation between the density of familial history of alcoholism and adolescent alcohol use. Additionally, the present study tested the direct effect of LR to negative, sedative alcohol effects on adolescent drinking over and above the effects of sensation seeking; and also tested the moderating effect of sensation seeking on the relation between level of response negative, sedative alcohol effects and adolescent drinking. Specifically, OLS regression models first estimated the effects of sensation seeking, LR to negative, sedative alcohol effects, and their interaction on alcohol outcomes, over and above the influence of covariates. Indirect effects were then tested using the PRODCLIN method through RMediation. Analyses failed to support sensation seeking as a mediator in the relation between familial history of alcoholism and adolescent drinking, and as a moderator of the relation between LR and adolescent drinking. However, analyses did support a robust direct effect of LR to negative, sedative alcohol effects on adolescent alcohol involvement. A significant mediating effect of initial LR to negative, sedative alcohol effects on the relation between familial alcoholism and adolescent drinking was found, however failed to maintain significance in post-hoc analyses attenuating the downward bias of the measure of initial LR. Initial LR to negative, sedative alcohol effects continued to predict adolescent drinking after attenuating measure bias. These findings strengthen research on initial LR to negative, sedative alcohol effects as a risk for greater alcohol involvement in adolescence, and underscore the complexity of studying the familial transmission of alcoholism in adolescent populations

Women are exposed to numerous endogenous and exogenous hormones across the lifespan. In the last several decades, the prescription of novel hormonal contraceptives and hormone therapies (HTs) have resulted in aging women that have a unique hormone exposure history; little is known about the impact of these hormone exposures on short- and long- term brain health. The goal of my dissertation was to understand how lifetime hormone exposures shape the female cognitive phenotype using several innovative approaches, including a new human spatial working memory task, the human radial arm maze (HRAM), and several rodent menopause models with variants of clinically used hormone treatments. Using the HRAM (chapter 2) and established human neuropsychological tests, I determined males outperformed females with high endogenous or exogenous estrogen levels on visuospatial tasks and the spatial working memory HRAM (chapter 3). Evaluating the synthetic estrogen in contraceptives, ethinyl estradiol (EE), I found a high EE dose impaired spatial working memory in ovariectomized (Ovx) rats, medium and high EE doses reduced choline-acetyltransferace-immunoreactive neuron population estimates in the basal forebrain following Ovx (chapter 4), and low EE impaired spatial cognition in ovary-intact rats (chapter 5). Assessing the impact of several clinically-used HTs, I identified a window of opportunity around ovarian follicular depletion outside of which the HT conjugated equine estrogens (CEE) was detrimental to spatial memory (chapter 6), as well as therapeutic potentials for synthetic contraceptive hormones (chapter 9) and bioidentical estradiol (chapter 7) during and after the transition to menopause. Chapter 6 and 7 findings, that estradiol and Ovx benefitted cognition after the menopause transition, but CEE did not, are perhaps due to the negative impact of ovarian-produced, androstenedione-derived estrone; indeed, blocking androstenedione’s conversion to estrone prevented its cognitive impairments (chapter 8). Finally, I determined that EE combined with the popular progestin levonorgestrel benefited spatial memory during the transition to menopause, a profile not seen with estradiol, levonorgestrel, or EE alone (chapter 9). This work identifies several cognitively safe, and enhancing, hormonal treatment options at different time points throughout female aging, revealing promising avenues toward optimizing female health.

There are significant and wide-ranging health benefits of physical activity, yet the majority of adolescents in the United States do not engage in the recommended amount. This poses a significant public health challenge. Parents have a substantial influence on adolescents' levels of activity, indicating that parenting may be an especially salient target of interventions designed to promote physical activity. The current study tested the hypothesis that a family intervention to promote effective parenting would have a positive collateral effect on adolescent physical activity. This study also tested whether the increase in activity was mediated by changes in parental monitoring and family relationship quality. Furthermore, the current study assessed whether adolescent gender moderated the relationship between parental monitoring and physical activity, such that increased parental monitoring predicted increases in physical activity for girls, but not for boys. Participants were 232 adolescents at risk for behavior problems drawn from a larger randomized controlled trial of the Family Check-Up. Adolescents completed questionnaires and participated in a family assessment with their caregivers in the 6th through 9th grades. Youth randomized to the intervention reported significantly more physical activity at follow-up relative to controls. Results failed to confirm the role of family factors as mediators of the effect of the intervention on physical activity. When gender was considered as a moderator, it appeared that parental monitoring was strongly and positively correlated with physical activity for girls, but not for boys. While the mechanism by which the Family Check-Up leads to increased physical activity remains unclear, its robust effects suggest that family intervention can be used to promote physical activity and might therefore have further-reaching health benefits.

Methods to test hypotheses of mediated effects in the pretest-posttest control group design are understudied in the behavioral sciences (MacKinnon, 2008). Because many studies aim to answer questions about mediating processes in the pretest-posttest control group design, there is a need to determine which model is most appropriate to test hypotheses about mediating processes and what happens to estimates of the mediated effect when model assumptions are violated in this design. The goal of this project was to outline estimator characteristics of four longitudinal mediation models and the cross-sectional mediation model. Models were compared on type 1 error rates, statistical power, accuracy of confidence interval coverage, and bias of parameter estimates. Four traditional longitudinal models and the cross-sectional model were assessed. The four longitudinal models were analysis of covariance (ANCOVA) using pretest scores as a covariate, path analysis, difference scores, and residualized change scores. A Monte Carlo simulation study was conducted to evaluate the different models across a wide range of sample sizes and effect sizes. All models performed well in terms of type 1 error rates and the ANCOVA and path analysis models performed best in terms of bias and empirical power. The difference score, residualized change score, and cross-sectional models all performed well given certain conditions held about the pretest measures. These conditions and future directions are discussed.

Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent with multivariate normal data. However, less is known about the similarities and differences of these two approaches with multilevel data, and the methodological literature provides no insight into the situations under which the approaches would produce identical results. This document examined five multilevel multiple imputation approaches (three JM methods and two FCS methods) that have been proposed in the literature. An analytic section shows that only two of the methods (one JM method and one FCS method) used imputation models equivalent to a two-level joint population model that contained random intercepts and different associations across levels. The other three methods employed imputation models that differed from the population model primarily in their ability to preserve distinct level-1 and level-2 covariances. I verified the analytic work with computer simulations, and the simulation results also showed that imputation models that failed to preserve level-specific covariances produced biased estimates. The studies also highlighted conditions that exacerbated the amount of bias produced (e.g., bias was greater for conditions with small cluster sizes). The analytic work and simulations lead to a number of practical recommendations for researchers.