A Monte Carlo simulation study on class enumeration with latent class regression models. / Latent class regression (LCR) is a statistical method used to identify qualitatively different groups or latent classes within a heterogeneous population and commonly used in the behavioural, health, and social sciences. Despite the vast applications, an agreed fit index to correctly determine the number of latent classes is hotly debated. To add, there are also conflicting views on whether covariates should or should not be included into the class enumeration process. We conduct a simulation study to determine the impact of covariates on the class enumeration accuracy as well as study the performance of several commonly used fit indices under different population models and modelling conditions. Our results indicate that of the eight fit indices considered, the aBIC and BLRT proved to be the best performing fit indices for class enumeration. Furthermore, we found that covariates should not be included into the enumeration procedure. Our results illustrate that an unconditional LCA model can enumerate equivalently as well as a conditional LCA model with its true covariate specification. Even with the presence of large covariate effects in the population, the unconditional model is capable of enumerating with high accuracy. As noted by Nylund and Gibson (2016), a misspecified covariate specification can easily lead to an overestimation of latent classes. Therefore, we recommend to perform class enumeration without covariates and determine a set of candidate latent class models with the aBIC. Once that is determined, the BLRT can be utilized on the set of candidate models and confirm whether results obtained by the BLRT match the results of the aBIC. By separating the enumeration procedure of the BLRT, it still allows one to use the BLRT but reduce the heavy computational burden that is associated with this fit index. Subsequent analysis can then be pursued accordingly after the number of latent classes is determined. / Thesis / Master of Science (MSc)

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Abstract Objective The goal of this study is to uncover latent classes of maternal and paternal helicopter parenting among American and Chinese college students, and to examine whether latent classes of maternal and paternal helicopter parenting are related to college students’ mental health (depression and self-esteem). Background Previous studies have examined the association between helicopter parenting and college students’ well-being. However, less is known about how the multidimensional construct of helicopter parenting is related to college students’ mental health across Western and Eastern cultural contexts. Method We conducted three-step latent class analyses using nine helicopter parenting indicators for 1,386 mother–child and 1,214 father–child groups in the United States and 520 mother–child and 454 father–child groups in China. Next, we tested the association between the class membership of maternal and paternal helicopter parenting and college students’ mental health. Results We identified distinct helicopter parenting latent classes among four American and Chinese parent–child groups. We also found that American college students in the strong maternal helicopter parenting latent class reported poorer mental health than those in other latent classes. Conclusion Our findings indicate that the multidimensional construct of helicopter parenting can be interpreted differently by parents and college-aged children according to their social and cultural contexts. Implications The findings of this study suggest that it is necessary to strengthen understanding of the multidimensional construct of helicopter parenting for parents with college-aged children to enable them to develop more appropriate parenting practices as well as support their children's well-being.

Shelters often make euthanasia or adoption decisions based on the results of FeLV-FIV point-of-care tests but given the low estimated prevalence of these diseases and imperfect test performance, this might not be a good practice because of diagnostic error. The objectives of this study were to determine the true prevalence of FeLV and FIV in apparently healthy and sick shelter cats in Mississippi, estimate predictive value of the Zoetis Witness FeLV-FIV Rapid ImmunoMigration test results at the estimated true prevalences through Bayesian latent class modeling, and formulate testing recommendations for shelters. One chapter will review the literature on FeLV and FIV. The bulk of this thesis will focus on determining the true prevalence of retroviral infection in Mississippi shelter cat populations. The last chapter will use Bayesian modeling to estimate test performance and predictive value of test results in healthy and sick shelter cat populations.

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The aim of this study is to examine the public preferences for urban riparian corridors in arid ‎regions, by testing to what extent people are willing to trade-off unmaintained ecological ‎landscape for aesthetics offered by specific micro and ‎macro environmental factors. Landscape ‎design reflects ecological and aesthetic values, and trade-offs are often made ‎between the two in ‎‎practice. In arid regions, water scarcity means riparian corridors are the ‎richest landscape ‎typology and the only ‎blue-green links for hundreds of miles. Pressure from ‎urbanization and ‎lack of eco-literacy contribute to negative ‎feedback loops which present dire ‎challenges for ‎migrating avifauna and regional wildlife. Regarding natural ‎resources and ‎biodiversity, where ‎multiple deliverable ‎ecosystem services rely on the quality and health of that ‎‎ecosystem, riparian ‎systems with high biomass are more desirable. Although this can be ‎achieved with low or no ‎‎maintenance riparian buffers, these unmaintained ecological landscapes ‎play an intrinsic role in ‎sustaining the ‎global ecosystem services and are important for the survival of the inhabitants ‎‎(avifauna). Ecological ‎landscapes are often subjected to trade-offs with aesthetic ‎landscapes that ‎include micro and ‎macro environmental factors such as manicured landscapes. It is accepted that ‎‎there is a ‎preference for aesthetics in landscape design; however, it is unclear how laypeople ‎prioritize ‎aesthetics ‎over different ecological factors in landscape scenes. This study uses a ‎Discrete Choice ‎Experiment (DCE) to elicit the ‎preferences of current or pretendant residents of ‎Jeddah City, Saudi Arabia for multiple landscape scenes. The method ‎combines ecological ‎landscape characteristics adopted from ‎the QBR index that are found in the study area in Jeddah ‎and aesthetic ‎characteristics, such as micro and macro environmental factors that are commonly ‎suggested in landscape design projects adapted from relevant visual preference studies (Alsaiari, ‎‎2018; Kenwick et al., 2009; Kuper‎ ‎,‎2017; Zhao et al., 2017). ‎DCE is a widely used method to ‎reveal preferences by analyzing the trade-offs people make ‎between ‎alternatives. Participants in ‎this study were exposed to a set of designs, which included ‎various configurations of ‎aesthetic ‎and ecological elements. Participants' choices revealed the influence of their ecological and ‎‎aesthetic values. Results show that minimal design interventions would prevent trading off the ‎ecological unmaintained landscape and that there are four subgroups with distinct homogeneous ‎preferences for the attributes affecting the appeal for the urban riparian corridor in Jeddah City. ‎Finally, results show that even though there are significant differences between subgroups based ‎on preferences, the demographic information is proportionally distributed in a way the means ‎differences diminish between the subgroups. ‎Findings in this study will equip ‎decision-makers ‎with operational definitions relating to riparian ‎landscape design and a method ‎that they can use ‎to minimize losses in ecological value over aesthetic value. This study will help‎ ‎researchers and ‎landscape architects advance visual preference research further into the domain ‎of empirical ‎‎studies.‎ ‎ / Doctor of Philosophy / Landscape architecture is a profession that entails planning and design outdoor spaces, ‎‎landmarks, and ‎structures to improve the built environment and increasing the quality of ‎‎people's lives by achieving ‎environmental, social, economic, and aesthetic outcomes‎. The ‎profession often reflects ecological and ‎aesthetic values, and trade-offs are often made ‎between ‎the two in ‎practice. These ecological values ‎represent environmental characteristics that are ‎important for the survival of wildlife (protected path ‎and safe habitat) and the overall ecosystem ‎‎(every being has a role that sustain the health of the ‎environment). Culturally, human is ‎accustomed to a slick-and-clean (tamped) looking plant within urban ‎developments (i.e., ‎neighborhoods). An example of the trade-off that often happen in practice between ‎the ‎ecological and aesthetic values is replacing an ecologically unmaintained plants that play ‎important ‎ecological role (i.e., wildlife habitat) with clean tamped plants to increase the value of ‎a real estate. Due ‎to the uncertainty surrounding people's acceptance of the features of these ‎ecological unmaintained ‎plants, especially when it entails introducing ecological riparian ‎landscape attributes within ‎neighborhoods for the first time, this dissertation focuses on both ‎assessing ecological elements ‎preferences within an urban arid region in Jeddah, Saudi Arabia ‎and assessing the extent to which ‎advanced analytical methods are capable of providing a better ‎understanding of ecological riparian ‎landscape attributes preference differences among a ‎seemingly homogenous sample of participants. ‎The increasing usage of manipulated images in ‎choice tasks inspired this dissertation. The results of the ‎study demonstrate that among the ‎relatively homogenous sample of participants that was recruited, ‎four significant preference ‎patterns have emerged, which could be used to describe and predict ‎preference for ecological ‎riparian landscape attributes and choice with great accuracy. The dissertation ‎also investigates ‎policy implications that might be beneficial in creating a ‎physical environment that ‎match public ‎preferences. ‎It also offers research implications and recommendations for landscape ‎architects ‎and urban designers on how to employ visual choice experiments, which have been well-‎‎developed in other research field

Background: Rates of accidental overdose mortality from substance use disorder (SUD) have risen dramatically in the United States since 1990. Between 1999 and 2004 alone rates increased 62% nationwide, with rural overdose mortality increasing at a rate 3 times that seen in urban populations. Cultural differences between rural and urban populations (e.g., educational attainment, unemployment rates, social characteristics, etc.) affect the nature of SUD, leading to disparate risk of overdose across these communities. Methods: Multiple-groups latent class analysis with covariates was applied to data from the 2011 and 2012 National Survey on Drug Use and Health (n=12.140) to examine potential differences in latent classifications of SUD between rural and urban adult (aged 18 years and older) populations. Nine drug categories were used to identify latent classes of SUD defined by probability of diagnosis within these categories. Once the class structures were established for rural and urban samples, posterior membership probabilities were entered into a multinomial regression analysis of socio-demographic predictors' association with the likelihood of SUD latent class membership. Results: Latent class structures differed across the sub-groups, with the rural sample fitting a 3-class structure (Bootstrap Likelihood Ratio Test P value=0.03) and the urban fitting a 6-class model (Bootstrap Likelihood Ratio Test P valueThis result supports the hypothesis that different underlying elements exist in the two populations that affect SUD patterns, and thus can inform the development of surveillance instruments, clinical services, and prevention programming tailored to specific communities.

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In order to obtain a closer look into the relationships between an at-risk populations’ family stressors and future school problem behaviors, a Latent Class Analysis (LCA) of family stressor variables was performed on at-risk elementary school girls from Health Zone 1. Participants were 308 girls with a mean age of 8.79 years. The dataset was inherited from the Delores Barr Weaver Policy Center and analyses were run to uncover latent classes of family stressors. Class membership was then utilized to predict future behavioral referrals and suspensions from school. A total of three classes emerged from the LCA: “Exposure to Trauma”; “Familial Stress”; and “Stable Home.” Chi-square analysis between class membership and future behavioral referrals and suspensions failed to reach significance. However, chi-square analyses between class membership and some future family stressors were significant. It appears that latent classes of stressors can be uncovered, and these classes can be utilized in the meaningful prediction of outcome variables. Implications for researchers and policy makers are discussed.

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