In this paper we head for a fully Bayesian analysis of the latent class model with a priori unknown number of classes. Estimation is carried out by means of Markov Chain Monte Carlo (MCMC) methods. We deal explicitely with the consequences the unidentifiability of this type of model has on MCMC estimation. Joint Bayesian estimation of all latent variables, model parameters, and parameters determining the probability law of the latent process is carried out by a new MCMC method called permutation sampling. In a first run we use the random permutation sampler to sample from the unconstrained posterior. We will demonstrate that a lot of important information, such as e.g. estimates of the subject-specific regression coefficients, is available from such an unidentified model. The MCMC output of the random permutation sampler is explored in order to find suitable identifiability constraints. In a second run we use the permutation sampler to sample from the constrained posterior by imposing identifiablity constraints. The unknown number of classes is determined by formal Bayesian model comparison through exact model likelihoods. We apply a new method of computing model likelihoods for latent class models which is based on the method of bridge sampling. The approach is applied to simulated data and to data from a metric conjoint analysis in the Austrian mineral water market. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

University, Diss., 2005--Düsseldorf.

abstract: Parenting approaches that are firm yet warm (i.e., authoritative parenting) have been found to be robustly beneficial for mainstream White Americans youths, but do not demonstrate similarly consistent effects among Chinese Americans (CA) adolescents. Evidence suggests that CA adolescents interpret and experience parenting differently than their mainstream counterparts given differences in parenting values and child-rearing norms between traditional Chinese and mainstream American cultures. The current study tests the theory that prospective effects of parenting on psychological and academic functioning depends on adolescents' cultural frameworks for interpreting and understanding parenting. CA adolescents with values and expectations of parenting that are more consistent with mainstream American parenting norms were predicted to experience parenting similar to their White American counterparts (i.e., benefiting from a combination of parental strictness and warmth). In contrast, CA adolescents with parenting values and expectations more consistent with traditional Chinese parenting norms were predicted to experience parenting and its effects on academic and psychological outcomes differently than patterns documented in the mainstream literature. This study was conducted with a sample of Chinese American 9th graders (N = 500) from the Multicultural Family Adolescent Study. Latent Class Analysis (LCA), a person-centered approach to modeling CA adolescents' cultural frameworks for interpreting parenting, was employed using a combination of demographic variables (e.g., nativity, language use at home, mother's length of stay in the U.S.) and measures of parenting values and expectations (e.g., parental respect, ideal strictness & laxness). The study then examined whether prospective effects of parenting behaviors (strict control, warmth, and their interaction effect) on adolescent adjustment (internalizing and externalizing symptoms, substance use, and GPA) were moderated by latent class membership. The optimal LCA solution identified five distinct cultural frameworks for understanding parenting. Findings generally supported the idea that effects of parenting on CA adolescent adjustment depend on adolescents' cultural framework for parenting. The classic authoritative parenting effect (high strictness and warmth leads to positive outcomes) was found for the two most acculturated groups of adolescents. However, only one of these groups overtly endorsed mainstream American parenting values. / Dissertation/Thesis / Ph.D. Psychology 2011

Efforts to objectively inform cannabis discourses include research on the epidemiology of cannabis abuse and dependence disorders or, collectively, cannabis use disorder (CUD). For my dissertation I identified classes of individuals based on intraindividual CUD trajectory patterns and contrasted trajectory classes with respect to clinical characteristics of CUD, developmental risk factors, and psychosocial outcomes. Identifying differences between trajectory classes provides evidence for the validity of trajectory-based CUD constructs and informs the development of comprehensive models of CUD epidemiology and trajectory-specific intervention approaches. My dissertation used data from the Oregon Adolescent Depression Project, a prospective epidemiological study of the psychiatric and psychosocial functioning of a representative community-based sample randomly selected from nine high schools across western Oregon. Four waves of data collection occurred between mid-adolescence and early adulthood and included diagnostic interviews and self-report questionnaires. Onset and offset ages of all CUD episodes were recorded. The reference sample included 816 participants who completed all diagnostic interviews. A series of latent class growth models revealed three distinct CUD trajectory classes through age 30: (1) a persistent increasing risk class; (2) a maturing out class, marked by increasing risk through age 20 and then a decreasing risk through early adulthood; and (3) a stable low risk class. Rates of cannabis dependence were similar across the persistent increasing and the maturing out classes. Trajectory classes characterized by a history of CUD were associated with a variety of childhood risk factors and measures of psychosocial functioning during early adulthood. Participants who were male, had externalizing disorders, and had psychotic experiences during early adulthood discriminated between the persistent increasing and the maturing out classes. Future research based on more diverse samples is indicated, as are well-controlled tests of associations between risk factors, trajectory class membership, and psychosocial outcomes. A better understanding of these relationships will inform etiological theories of CUD and the development of effective intervention programs that target problematic cannabis use at specific developmental stages. Designing targeted versus undifferentiated interventions for those at greatest risk for adult psychosocial impairment could be a cost-effective way to mitigate the consequences of CUD.

abstract: The purpose of this study was to examine under which conditions "good" data characteristics can compensate for "poor" characteristics in Latent Class Analysis (LCA), as well as to set forth guidelines regarding the minimum sample size and ideal number and quality of indicators. In particular, we studied to which extent including a larger number of high quality indicators can compensate for a small sample size in LCA. The results suggest that in general, larger sample size, more indicators, higher quality of indicators, and a larger covariate effect correspond to more converged and proper replications, as well as fewer boundary estimates and less parameter bias. Based on the results, it is not recommended to use LCA with sample sizes lower than N = 100, and to use many high quality indicators and at least one strong covariate when using sample sizes less than N = 500. / Dissertation/Thesis / M.A. Psychology 2012

This dissertation explored the academic growth in mathematics of a longitudinal cohort of 21,567 Oregon students during middle school on a state accountability test. The student test scores were used to calculate estimates of school performance based on four different accountability models (percent proficient [PP], change in PP, multilevel growth, and growth mixture). On average, 72% of Oregon eighth graders were proficient in mathematics in 2012, 71% in the average school, and 6% more students in this cohort demonstrated mathematics proficiency compared to 2011. The two-level unconditional multilevel growth model estimated the average intercept (Grade 6) to be 228.4 (SE = 0.07) scale score points with an average middle school growth rate of 5.40 scale points per year (SE = 0.02) on the state mathematics test. Student demographic characteristics were a statistically significant improvement on the unconditional model. A major shortcoming of this research, however, was the inability to find successful model convergence for any three-level growth model or any growth mixture model. A latent class growth analysis was used to uncover groups of students who shared common growth trajectories. A five-latent class solution best represented the data with the lowest BIC and a significant LMR p. Two of the latent classes were students who had high achievement in Grade 6 and demonstrated high growth across middle school and a second group with low sixth grade achievement that had below average growth in middle school. Student-level demographic predictors had statistically significant relations with growth characteristics and latent class membership. In comparing school performance based on the four different models, it was found that, although statistically correlated, the models of school performance ranked schools differently. A school’s percentage of proficient students in Grade 8 correlated moderately (r = [.60, .70]) with growth over the middle school years as estimated by the growth and LCGA models. About 70% to 80% of schools ranked more than 10 percentiles differently for every pairwise comparison of models. These results, like previous research call into question whether currently used models of school performance produce consistent and valid descriptions of school performance using state test scores.

abstract: The victim-offender overlap is a widely accepted empirical fact in criminology. While many methodological strategies have been used to study overlap, prior studies have assumed that it is uniform, taking little consideration into the potential differences within the overlap. The larger body of criminological research on pathways to crime suggests that victim-offenders also have variability in their victimization experiences and offending patterns. Not accounting for variation within the overlap has produced inconsistent findings in terms of establishing theoretical explanations for the victimization and offending relationship. Several general theories of crime have merit in their assumptions about the relationship between victimization and offending. Routine activity/lifestyle theory, low self-control theory, and general strain theory offer insight into the overlap. Variables derived from these three general theories are assessed to test their ability to explain a more complex conceptualization of the victim-offender overlap. Using data on 3,341 individuals drawn from four waves of the publically available National Longitudinal Study of Adolescent to Adult Health (Add Health), a latent class analysis establishes unique victim-offender overlap taxonomies. A multinomial logistic regression is conducted to test how well theoretically derived variables from three general theories (e.g., routine activity theory, low self-control theory, and general strain theory) predict membership in the unique victim-offender overlap taxonomies. Additional multinomial logistic regressions are run using a split sample analyses to test the invariance of the findings across different social groupings (e.g., gender and race/ethnicity). Comparing the more complex operationalization of the victim-offender overlap with the baseline regression models shows notable differences. For example, depression significantly predicts membership in the general victim-offender overlap group, but when taking into consideration variation within the overlap, depression does not consistently predict membership in all taxonomies. Similar results are found for routine activity/lifestyle theory and low self-control theory. Tests of invariance across gender and race/ethnicity highlight the need to consider how theoretical explanations of the victim-offender overlap differ based on social groupings. Males and females have unique risks and needs and these should be reflected in how routines and negative emotions are measured. The findings underscore the need to consider overlap when studying the relationship between victims and offenders. Implications for theory, future research, and policy are also discussed. / Dissertation/Thesis / Doctoral Dissertation Criminology and Criminal Justice 2018

abstract: This dissertation examined Mexican American individuals' romantic relationships within two distinct developmental periods, adolescence and adulthood. Study 1 used latent class analysis to explore whether 12th grade Mexican Americans' (N = 218) romantic relationship characteristics, cultural values, and gender created unique romantic relationship profiles. Results suggested a three-class solution: higher quality, satisfactory quality, and lower quality romantic relationships. Subsequently, associations between profiles and adolescents' adjustment variables were examined via regression analyses. Adolescents with higher and satisfactory quality romantic relationships reported greater future family expectations, higher self-esteem, and fewer externalizing symptoms than adolescents with lower quality romantic relationships. Similarly, adolescents with higher quality romantic relationships reported greater academic self-efficacy and fewer sexual partners than adolescents with lower quality romantic relationships. Finally, adolescents with higher quality romantic relationships also reported greater future family expectations and higher academic self-efficacy than adolescents with satisfactory quality romantic relationships. To summarize, results suggested that adolescents engaged in three unique types of romantic relationships with higher quality being most optimal for their adjustment. Study 2 used latent growth modeling to examine marital partners' (N = 466) intra- and inter-individual changes of acculturative stress, depressive symptoms, and marital quality. On average across the seven years, husbands' acculturative stress remained steady, but wives' significantly decreased; partners' depressive symptoms remained relatively steady, but their marital quality significantly decreased. Although partners' experiences of acculturative stress were less similar than their experiences of depressive symptoms and marital quality, overall their experiences were interconnected. Significant spillover and crossover effects emerged between partners' initial levels of acculturative stress and depressive symptoms and between depressive symptoms and marital quality. Moreover, changes in husbands' depressive symptoms were negatively associated with changes in their marital quality. Overall, results suggested that partners' experiences were interconnected across time. / Dissertation/Thesis / Ph.D. Family and Human Development 2014

Background: Given the complex and progressive nature of Alzheimer's disease (AD), a precision medicine approach for diagnosis and treatment requires the identification of patient subgroups with biomedically distinct and actionable phenotype definitions. Methods: Longitudinal patient-level data for 1160 AD patients receiving placebo or no treatment with a follow-up of up to 18 months were extracted from an integrated clinical trials dataset. We used latent class mixed modelling (LCMM) to identify patient subgroups demonstrating distinct patterns of change over time in disease severity, as measured by the Alzheimer's Disease Assessment Scale-cognitive subscale score. The optimal number of subgroups (classes) was selected by the model which had the lowest Bayesian Information Criterion. Other patient-level variables were used to define these subgroups' distinguishing characteristics and to investigate the interactions between patient characteristics and patterns of disease progression. Results: The LCMM resulted in three distinct subgroups of patients, with 10.3% in Class 1, 76.5% in Class 2 and 13.2% in Class 3. While all classes demonstrated some degree of cognitive decline, each demonstrated a different pattern of change in cognitive scores, potentially reflecting different subtypes of AD patients. Class 1 represents rapid decliners with a steep decline in cognition over time, and who tended to be younger and better educated. Class 2 represents slow decliners, while Class 3 represents severely impaired slow decliners: patients with a similar rate of decline to Class 2 but with worse baseline cognitive scores. Class 2 demonstrated a significantly higher proportion of patients with a history of statins use; Class 3 showed lower levels of blood monocytes and serum calcium, and higher blood glucose levels. Conclusions: Our results, 'learned' from clinical data, indicate the existence of at least three subgroups of Alzheimer's patients, each demonstrating a different trajectory of disease progression. This hypothesis-generating approach has detected distinct AD subgroups that may prove to be discrete endophenotypes linked to specific aetiologies. These findings could enable stratification within a clinical trial or study context, which may help identify new targets for intervention and guide better care.

This thesis addresses various food safety issues and investigates them from an economic perspective within four different, but related, studies. The studies are intended to provide policy-makers and other decision-makers in the industry with valuable information that will help them to implement better mitigation strategies and policies. The studies also present some applications of advancements in choice modelling, and thus contribute to the literature. To address these issues, various surveys were conducted in the UK.The first study investigates different stakeholder groups’ perceptions of responsibility among the stages of the meat chain for ensuring the meat they eat does not cause them to become ill, and how this differed with food types. The means by which this is achieved is novel, as we elicit stakeholders’ relative degrees of responsibility using the Best-Worst Scaling (BWS) technique. BWS is particularly useful because it avoids the necessity of ranking a large set of items, which people have been found to struggle with. The results from this analysis reveal a consistent pattern among respondents of downplaying the extent of their own responsibility. The second study explores people’s perceptions of various food and non-food risks within a framework characterised by the level of control that respondents believe they have over the risks, and the level of worry that the risks prompt. The means by which this is done differs from past risk perception analyses in that it questions people directly regarding their relative assessments of the levels of control and worry over the risks presented. The substantive analysis of the risk perceptions has three main foci concerning the relative assessment of (i) novel vs. more familiar risks, (ii) food vs. non-food risks, (iii) differences in the risk perceptions across farmers and consumers, with a particular orientation on E. coli. The third study investigates consumers’ willingness to pay (WTP) for reductions in the level foodborne health risk achieved by (1) nanotechnology and (2) less controversial manners in the food system. The difference between consumers’ valuations provides an implicit value for nanotechnology. This comparison is achieved via a split sample Discrete Choice Experiment study. Valuations of the risk reductions are derived from conditional, heteroskedastic conditional, mixed, and heteroscedastic mixed logit models. General results show the existence of heterogeneity in British consumers’ preferences and variances, and that the value of nanotechnology differs for different types of consumers. The fourth study investigates consumers’ perceptions of trust in institutions to provide information about nanotechnology and its use in food production and packaging. It is shown how the use of BWS and Latent Class modelling of survey data can provide in-depth information on consumer categories useful for the design of effective public policy, which in turn would allow the development of best practice in risk communication for novel technologies. Results show heterogeneity in British consumers’ preferences. Three distinct consumer segments are identified: Class-1, who trust “government institutions and scientists” most; Class-2, who trust “non-profit organisations and environmental groups” most; and Class-3, who trust “food producers and handlers, and media” most.