Although there has been a recent surge in research examining comorbidity between externalizing and internalizing disorders in childhood, relatively less work has examined relations between specific externalizing conditions (i.e., oppositional defiant disorder (ODD) symptoms) and their co-occurrence with specific internalizing conditions (i.e., depressive symptoms). Furthermore, little empirical work has evaluated potential underlying processes, such as emotion dysregulation, which may explain relations between co-occurring ODD and depressive symptoms. There is also a paucity of research examining developmental patterns of co-occurring ODD and depressive symptoms. In the present study, I used latent class and latent transition analyses to (a) identify groups of children based on ODD and depressive symptom levels, (b) determine whether emotion dysregulation predicted co-occurring ODD and depressive symptoms, and (c) examine developmental patterns of change and continuity in groups across middle childhood within a community-based sample. Children were characterized by three latent classes based on ODD and depressive symptom severity: a group with very low levels of ODD or depressive symptoms, an ODD-only group with low levels of symptoms, and a co-occurring ODD and depressive symptom group with moderate levels of ODD and low levels of depressive symptoms. Furthermore, emotion dysregulation predicted to the class with moderate levels of ODD and low levels of depressive symptoms, although prediction from emotion dysregulation to class membership depended on the methodology used to index emotion dysregulation. Results of the LTA analyses suggested that symptom severity was relatively stable across middle childhood, with little evidence of changes in developmental patterns of ODD and depressive symptoms. Overall, the results of this study provide an important foundation for more sophisticated empirical inquiry regarding co-occurring ODD and depressive symptoms in childhood and potential processes that may explain their onset and development. / Psychology

Studies suggest that nearly two-thirds of women fake or have faked orgasm, yet few researchers have explicitly examined this phenomenon. Previous studies have identified some group differences between women who fake orgasm and those who do not on dimensions of sexual experience, emotion regulation, intimacy, relationship status, and sexual functioning. To date, research into this phenomenon has relied solely on variable-centered analyses (e.g., exploratory factor analysis, correlation, and regression). This study used a person-centered approach (i.e., latent class analysis; LCA) to explore differences in women's motives across individuals, using scores from the Faking Orgasm Scale. A 5-class model was determined to be most interpretable and the best fitting to the data. Classes included low, moderate, and high frequency faking orgasm, partner-focused faking orgasm, and pleasure-focused faking orgasm. These classes were then compared on dimensions of sexual functioning, intimacy, and emotion regulation, as well as demographic variables (e.g., age, length of relationship, number of sexual partners). Significant differences were found in sexual desire, sexual activity, and orgasmic consistency, but not in sexual satisfaction. Significant differences were also evidenced in intimacy, general level of emotion dysregulation, and across various dimensions of emotion regulation. No differences across classes were revealed on age, length of relationship, or number of sexual partners. These findings can serve as the foundation for further exploration into understanding women's various styles of interacting sexually with a partner and may have implications for couples therapy, sex therapy, and individual interventions for women struggling with physical and/or emotional intimacy with a partner. / Psychology

A multitude of research demonstrates that ADHD is associated with negative psychological correlates and outcomes among children, such as academic difficulties and peer relationship problems. Youth with ADHD also experience high rates of comorbidity or co-occurring conditions, including mood, anxiety, oppositional defiant, and conduct disorders. However, few studies have investigated the development of co-occurring psychological symptoms among youth with ADHD over time and across different developmental periods. Shared risk factors likely contribute to the development of ODD, CD, anxiety, and depression among youth with ADHD. Emotion regulation and parenting style may confer risk or resilience for the development of co-occurring symptoms, but research is wanting. The current study examined an existing sample of youth who were recruited at age 10-12 and were followed at age 12-14 and 16. Analyses aimed to (a) identify subgroups of youth varying in type and levels of ADHD and co-occurring symptoms at three different time points using latent class analyses, (b) examine stability of membership and transitions to classes that differ in levels of ADHD and co-occurring symptoms using latent transition analyses, and (c) investigate emotion regulation and parenting style as predictors of stability and transitions among classes. Results revealed different patterns of ADHD and co-occurring symptoms, including a Low Symptoms class at each time point. Classes of youth with ADHD+Externalizing problems and ADHD+Internalizing problems emerged at ages 10-12 and 12-14. At age 16, two classes with qualitatively and quantitatively different externalizing and internalizing symptoms were identified. Latent transition analyses revealed transitions into the Low Symptoms class from each time point, but also stability and transitions to other symptomatic classes. Predictor analyses indicated that emotion regulation and parenting style were associated with transitions among and stability within classes, but findings were dependent on whether classes were defined primarily by co-occurring externalizing or internalizing symptoms. Results of the present study indicate that children with ADHD are likely to exhibit a range of psychological symptoms, but the frequency and quality of co-occurring symptoms may change over time. Emotion regulation and parenting may be potential targets for enhanced interventions among youth with ADHD with and without co-occurring symptoms. / Psychology

Background and Objectives: Understanding patient and public values and preferences is essential to healthcare and policy decision making. Discrete choice experiments (DCEs) are a common tool used to capture and quantify these preferences. Recent technological advances allow for a variety of approaches to create and analyze DCEs. However, there is no optimal DCE design, nor analysis method. Our objectives were to (i) survey DCE simulation studies to determine what design features affect statistical efficiency, and assess their reporting, (ii) further investigate these findings with a de novo simulation study, and (iii) explore the sensitivity of individuals’ preference of attributes to several methods of analysis. Methods: We conducted a systematic survey of simulation studies within the health literature, created a DCE simulation study of 3204 designs, and performed two empirical comparison studies. In one empirical comparison study, we determined addiction agency employees’ preferences on knowledge translation attributes using four models, and in the second, we determined elementary school children’s choice of bullying prevention programs using nine models. Results and Conclusions: In our evaluation of DCE designs, we identified six design features that impact the statistical efficiency of a DCE, several of which were further investigated in our simulation study. The reporting quality of these studies requires improvement to ensure that appropriate inferences can be made, and that they are reproducible. In our empirical comparison of statistical models to explore the sensitivity of individuals preferences of attributes, we found similar rankings in the relative importance measures of attributes’ mean part-worth utility estimates, which differed when using latent class models. Understanding the impact of design features on statistical efficiency are useful for designing optimal DCEs. Incorporating heterogeneity in the analysis of DCEs may be important to make appropriate inferences about individuals’ preferences of attributes within a population. / Thesis / Doctor of Philosophy (PhD) / This thesis focuses on the design and analysis of preference surveys, which are referred to as discrete choice experiments. These surveys are used to capture and quantify individuals’ preferences on various characteristics describing a product or service. They are applied in various health settings to better understand a population. For example, clinicians may want to further understand a patient population’s preferences in regards to multiple treatment alternatives. Currently, there is no optimal approach for designing or analyzing preference surveys. We investigated what factors help improve the design of a preference survey by exploring the literature and conducting our own simulation study. We also investigated how sensitive the results of a preference survey were based on the statistical model used. Overall, we found that (i) increasing the amount of information presented and reducing the number of variables to explore will maximize the statistical optimality of the survey; and (ii) analyzing the data with different statistical models will yield similar results in the ranking of individuals’ preferences of the variables explored.

The Virginia Guidelines for Uniform Performance Standards and Evaluation Criteria for Teachers represented a significant overhaul of conventional teacher evaluation criteria in Virginia. The policy outlined seven performance standards by which all Virginia teachers would be evaluated. This study explored the application of cognitive diagnostic modeling to measure teachers' perceptions of intra-organizational mechanisms available to support educational professionals in implementing this policy. It was found that a coarse-grained, four-attribute compensatory, re-parameterized unified model (C-RUM) fit teacher perception data better and had lower standard errors than the competing finer-grained models. The Q-matrix accounted for the complex loadings of items to the four theoretically and empirically driven mechanisms of implementation support including characteristics of the policy, teachers, leadership, and the organization. The mechanisms were positively, significantly, and moderately correlated which suggested that each mechanism captured a different, yet related, component of policy implementation support. The diagnostic profile estimates indicated that the majority of teachers perceived support on items relating to "characteristics of teachers." Moreover, almost 60% of teachers were estimated to belong to profiles with perceived support on "characteristics of the policy." Finally, multiple group multinomial log-linear models (Xu and Von Davier, 2008) were used to analyze the data across subjects, grade levels, and career status. There was lower perceived support by STEM teachers than non-STEM teachers who have the same profile, suggesting that STEM teachers required differential support than non-STEM teachers. The precise diagnostic feedback on the implementation process provided by this application of diagnostic models will be beneficial to policy makers and educational leaders. Specifically, they will be better prepared to identify strengths and weaknesses and target resources for a more efficient, and potentially more effective, policy implementation process. It is assumed that when equipped with more precise diagnostic feedback, policy makers and school leaders may be able to more confidently engage in empirical decision making, especially in regards to targeting resources for short-term and long-term organizational goals subsumed within the policy implementation initiative. / Ph. D.

Labeling is a product differentiation mechanism which has increased in prevalence across many markets. This study investigated the potential for a labeling program applied in ornamental plant sales, given key ongoing issues affecting ornamental plant producers: irrigation water use and plant disease. Our research investigated how to better understand the market for plants certified as disease free and/or produced using water conservation techniques through segmenting the market by consumers' environmental preferences. Latent class analysis was conducted using choice modeling survey results and respondent scores on the New Environmental Paradigm scale. The results show that when accounting for environmental preferences, consumers can be grouped into two market segments. Relative to each other, these segments are considered: price sensitive and attribute sensitive. Our research also investigated market segments' preferences for multiple certifying authorities. The results strongly suggest that consumers of either segment do not have a preference for any particular certifying authority. / Master of Science

Insufficient knowledge of residential preferences represents a major obstacle to achieving residential satisfaction and quality of life. This obstacle is even greater in the case of transit-oriented developments (TODs), as their success depends, in part, on the degree to which people's preferences are consistent with their residential environments. This study employed a visual choice experiment, which combines the benefits of visual preference surveys and discrete choice experiments, to elicit residential preference for TODs in Riyadh, Saudi Arabia, before the opening of its citywide public transportation system. Using a seemingly homogeneous sample of participants, the analysis incorporated three analytical methods to elicit residential preference: a multinomial logit model, a mixed logit model, and a latent class model. The results indicated the presence of preference heterogeneity and the emergence of four lifestyle classes that could explain and predict residential preference patterns. People with similar sociodemographic characteristics may have different lifestyles based on their choice behavior, marital status, and public transit attitudes. Additionally, the results showed a strong preference for low-density housing, even among those who favor living in a TOD; however, increasing density could be mitigated through the presence of other TOD attributes. The findings of this research point to the diversity of residential preferences and suggest that providing a variety of residential environments increases the likelihood that people will find their preferred environment. Additionally, planning efforts to convert all developments near transit, particularly in suburban locations, to TODs might be unsuitable in cities where public transportation has been introduced only recently. Instead, deferring TOD conversion efforts until public transportation and its use are mature may attract people to live near transit and encourage the gradual development of transit affinity in residents who may otherwise reject TOD living completely. Lastly, the successful application of a visual choice experiment in this research opens up a variety of potential analytical methods that are used commonly in other fields and have the potential to move visual preference research into the realm of robust empirical investigation. / Ph. D. / The work of urban planners, urban designers, architects, and policy makers centers on improving the built environment and increasing the quality of people’s lives. However, their work entails making decisions that are not always in tandem with people’s preferences (e.g., increasing housing density, proposing a mix of land uses in residential neighborhoods, introducing public transportation close to where people live and work, to name a few). Due to the uncertainty surrounding people’s acceptance of modifications of the built environment, especially when it entails introducing residential attributes for the first time, this dissertation focused on 1) assessing residential preference near public transportation nodes in Riyadh, Saudi Arabia before operation of the public transportation system, and 2) assessing the extent to which advanced analytical methods are capable of providing a better understanding of residential preference differences among a seemingly homogenous sample of participants. The work in this dissertation was motivated by the increasing use of manipulated images in choice tasks, where participants are presented with multiple images, each depicting a residential scenario, as bundles to choose from, and their choice patterns then recorded and analyzed. The results showed that among the relatively homogenous sample of participants that was recruited, four significant residential preference patterns have emerged, which could be used to describe and predict residential preference and choice with great accuracy. This dissertation laid out several policy implications that could be useful in providing a built environment that matches with what people want. It also provided research implications and suggestions on the use of visual choice experiments for urban planners and designers that are well-developed in other fields of inquiry.

Due to difficulties in elucidating neurobiological aspects of psychological disorders, the National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC), which encourages novel conceptualizations of the relationship between neurobiological circuitry and clinical difficulties. This approach is markedly different from the Diagnostic and Statistical Manual of Mental Disorders (DSM) based approach that has dominated clinical research to date. Thus, RDoC necessitates exploration of novel experimental and statistical approaches. Fear learning paradigms represent a promising methodology for elucidating connections between acute threat (“fear”) circuitry and fear-related clinical difficulties. However, traditional analytical approaches rely on central tendency statistics, which are tethered to a priori categories and assume homogeneity within groups. Growth Mixture Modeling (GMM) methods such as Latent Class Growth Analysis (LCGA) may be uniquely suited for examining fear learning phenotypes. However, just three extant studies have applied GMM to fear learning and only one did so in a human population. Thus, the degree to which classes identified in known studies represent characteristics of the general population and to which GMM methodology is applicable across populations and paradigms is unclear. This preliminary study applied LCGA to a fear learning lab study in an attempt to identify heterogeneity in fear learning patterns based on a posteriori classification. The findings of this investigation may inform efforts to move toward a trans-diagnostic conceptualization of fear learning. Consistent with the goals laid out in RDoC, explication of fear learning phenotypes may eventually provide critical information needed to spur innovation in psychotherapeutic and psychopharmacological treatment. / Master of Science / To date, most clinical psychology research has been based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is a catalog of mental health disorders that was originally designed to facilitate communication among clinicians. Many experts contend that this approach has hampered progress in the field of biological clinical psychology research. Thus, the National Institute of Mental Health (NIMH) created a new template for biological clinical psychology research called the Research Domain Criteria (RDoC). Since RDoC calls for a complete overhaul in the conceptualization of clinical dysfunction, this approach requires statistical and experimental innovation. One traditional experimental approach that may be helpful in understanding the RDoC topic of acute threat (“fear”) is called Pavlovian Fear Learning (PFL). However, traditional PFL studies have utilized statistical methods that are based on comparing group averages and require researchers to determine groups of interest based on theory before the study begins. This is problematic because RDoC calls for research that begins with evidence rather than theory. Growth Mixture Modeling (GMM) is a statistical methodology that may allow researchers to analyze fear learning data without having to begin with theoretically determined categories such as DSM disorders. However, little research has tested how well this approach would work. This study is just the second to apply a GMM approach to a human PFL study. The findings from this investigation may inform efforts to develop a statistical technique that is well suited for RDoCian research and may also spur innovation in psychotherapeutic and psychopharmacological treatment.

The Outcome Questionnaire (OQ-45; Lambert et al., 1996) has been widely employed as a psychotherapy outcome monitoring measure following research findings that support various aspects of its validity and sensitivity to change. Despite its broad usage in both clinical and research settings, some of its psychometric properties are not definite. The three subscales of the OQ-45 are designed to measure three distinct, but related, aspects of psychological functioning. However, neither the one- nor three-factor models have been supported by previous research. Likewise, the results of the current study supported neither of those factor structures. It was suspected that heterogeneity in data might have led to the lack of the confirmatory factor analysis model fit. Therefore, factor mixture modeling (FMM), a combination of confirmatory factor analysis and latent class analysis, was employed to investigate potential heterogeneity of the data. Among the series of factor mixture models with varying numbers of classes that were fitted, the two-class, unconditional FMM based on the revised three-factor solution was decided to best describe the data under analysis. Although three covariates of clinical status, sex, and race were selected as known sources of heterogeneity and incorporated into the FMMs (i.e., conditional model), the findings were contradictory to expectations. The implications of these findings in counseling were discussed in terms of aggregating OQ-45 scores and its score interpretation. Furthermore, this study demonstrates the process involved and dilemmas encountered in choosing the best fitting FMM. There is currently no criterion for assessing individual model fit. Instead, models’ fit are compared using various information criteria (IC). And, as was found in the current study, these ICs are frequently contradictory. Thus, the process of identifying the best fitting model cannot rest solely on fit indices but must also depend on interpretation of models and consideration of the ultimate use of the results. In the current study, consideration of transition matrices and the pattern of latent means across classes contributed as much to model selection as fit index interpretation. / text

Objectives: Adherence to psychotropic medication is poor among individuals with bipolar disorder (BD). To understand treatment experiences and associated adherence among these individuals, we developed a novel construct of Clinical Net Benefit (CNB) using psychiatric symptoms, adverse effects and overall functioning assessments. We tested whether adherence differed across classes of CNB, whether individuals transitioned between classes over time, and whether these transitions were differentially associated with adherence. Methods: Data come from individuals aged 18+ during five years of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Latent class analysis identified groups of CNB. Latent transition analysis determined probabilities of transitioning between classes over time. Adherence was defined as taking 75%+ of medications as prescribed. Associations between CNB and adherence were tested using multiple logistic regression adjusting for sociodemographic characteristics. Results: Five classes of CNB were identified during the first two years (high, moderately high, moderate, moderately low, low), and four classes (removing moderately high) during the last three years. Adherence did not differ across classes or time points. Medication regimens differed by class; those with higher CNB taking fewer medications had lower odds of adherence while those with lower CNB taking more medications had higher odds of adherence compared with monotherapy. Probability of transitioning from higher to lower CNB, and lower to higher CNB was greatest over time. Conclusions: CNB is heterogeneous in individuals treated for BD, and movement between classes is not uncommon. Understanding why individuals adhere despite suboptimal CNB may provide novel insights into aspects influencing adherence.