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The current study aimed to evaluate multiple longitudinal determinants of externalizing behavior problems in twins/triplets aged 7 to 12 years. Specifically, a prospective longitudinal design was utilized to assess relationships between age 5 sleep problems, age 5 temperament traits, and later externalizing problems. Additionally, heritability of sleep problems was assessed by utilizing the twin method, and genetic contributions of two specific genes – DRD4 and 5-HTTLPR – were evaluated. A total of 93 twins/triplets (40 boys and 53 girls) and their parents participated in the current study, and data were collected through self-report, parent-report, and molecular and behavioral genetic methods. Results suggest that sleep disturbances are significantly heritable, and that neither early sleep problems, temperament traits, nor specific genes significantly predicted follow-up externalizing problems. Post-hoc analyses assessing gene X environment interactions showed that externalizing problems were significantly predicted by the interaction between stressful life events and DRD4 risk, which is consistent with differential susceptibility models. This study has implications for future research as well as clinical practice, including for early screening, prevention, and intervention efforts aimed at decreasing childhood externalizing and sleep problems.

The Composite Rule Induction System proposed by Liang (1992) that uses the tabular approach and statistical inference to process qualitative and quantitative attributes separately for generating better classification rules. Yang (2007) extended the method by incorporating the second-order rules. This Study further extends the previous method by including a mechanism for detecting the existence of interaction effects. The detection method checks the degree of independence between attributes to determine whether the second-order rules should be processed. In order to evaluate the performance of the proposed method, an enhanced prototype system was developed and both real and simulated data were used to compare its accuracy and rule complexity with existing systems. The result shows that the enhanced system performs at least as accurate as the existing system but is significantly better in the complexity of the resulting knowledge base.

In recent decades, the growth of international trade and delocalisation been increasing with the advent of globalisation and nationalisation. Consequently, a proliferation of hybrid or bi-national products has emerged in the research of international marketing. A large body of studies agree that country of origin (COO) plays an important role on product evaluation, since a product consists of an array of information cues, and intrinsic (physical appearance) and extrinsic (price, brand or COO) compositions. There is a strong evidence that consumers usually use COO as one of the most important cues when making a judgement about a product. However, bi-national products raise a particular issue because they are associated to both country of manufacture (COM) and country of design (COD) constructs. The interaction effects between both cues are found to have a more powerlful effect than when only cue is presented (Bertoli and Resciniti, 2012). This study intends to decompose the COO construct into COM and COD constructs. Moreover, the roles of COM and COD on customers’ purchase intentions are investigated in order to provides practical implications for the sportswear industry, as well as consumption insights relating to the emerging market, Thailand. This research provides an in-depth examination of COM, COD and product involvement on consumers’ purchase intentions and customer perceived value (CPV). Thus, the research measures the effects of product familiarity in order to infer how consumers use country-related information in purchase decision-making. In addition, the research clarifies the role of CPV, which remains unclear regarding how consumers develop a product’s value through informational cues (COM and COD). Moreover, the previous research have only examined the outcomes of perceived quality as influenced by COO. Hence, this research intends to bring all dimensions of CPV to investigate whether the combined effects of COM and COD can have either a positive or a negative impact on customer’s value. A model of CPV is developed by Sweeney and Soutat (2001), which consists of quality, social, price and emotional values. A quantitative approach using experimental research design was conducted in Bangkok, Thailand for the purposes of this study, with a pilot test to ascertain the variables used in the main experiment: product involvement, favourable/least favourable COM and COD images. The experiment was designed to measure the relationship between constructs, of a high involvement product, namely sports trainers, and a low involvement product, namely sports t-shirt. The US and China were selected as the most and the least favourite COD, resepectively. Meanwhile, Japan and China were selected as the most and least favourite COM, respectively. Data analysis techniques such as analysis of varaince (ANOVA), multivariate analysis of variance (MANOVA), and t-test were employed for hypothesis testing. The results of this research demonstrate a comprehensive understanding of the interaction effects of country image and product involvement. The study confirms that COD is the salient cue for the evaluation of sportswear products in emerging countries. However, the level of product involvement does not influence the consumer’s decisions when COD and COM are both presented. Thus, the roles of COM, COD and product invovlement play an important role in evaluating overall CPV. The key contribution of this research is in its practical, theoretical and methodological implications to international marketing communications toward the sportswear industry. It offers the insights of two informational cues (COM and COD) to explain the phenomenon of bi-national products for unknown or new products.

Doctor of Philosophy / Department of Industrial & Manufacturing Systems Engineering / Zhijian Pei / Poor machinability of hard-to-machine materials (such as advanced ceramics and titanium) limits their applications in industries. Ultrasonic-vibration-assisted grinding (UVAG), a hybrid machining process combining material-removal mechanisms of diamond grinding and ultrasonic machining, is one cost-effective machining method for these materials. Compared to ultrasonic machining, UVAG has much higher material removal rate while maintaining lower cutting pressure and torque, reduced edge chipping and surface damage, improved accuracy, and lower tool wear rate. However, physics-based models to predict cutting force in UVAG have not been reported to date. Furthermore, edge chipping is one of the technical challenges in UVAG of brittle materials. There is no report related to effects of cutting tool design on edge chipping in UVAG of brittle materials. The goal of this research is to provide new knowledge of machining these hard-to-machine materials with UVAG for further improvements in machining cost and surface quality. First, a thorough literature review is given to show what has been done in this field. Then, a physics-based predictive cutting force model and a mechanistic cutting force model are developed for UVAG of ductile and brittle materials, respectively. Effects of input variables (diamond grain number, diamond grain diameter, vibration amplitude, vibration frequency, spindle speed, and federate) on cutting force are studied based on the developed models. Interaction effects of input variables on cutting force are also studied. In addition, an FEA model is developed to study effects of cutting tool design and input variables on edge chipping. Furthermore, some trends predicted from the developed models are verified through experiments. The results in this dissertation could provide guidance for choosing reasonable process variables and designing diamond tools for UVAG.

The present study investigated the direct and interactional effects of neighborhood disadvantage and harsh parenting on concurrent assessments and change in externalizing and internalizing behavior in toddlerhood. The study included 55 mothers and their children; families completed in-home assessments when children were 2 and 3 years of age. Mothers' reports were used to measure neighborhood disadvantage and children's problem behaviors. Observer ratings derived from a clean up task were used to measure harsh parenting. Four hierarchical regression equations were computed to test each study hypothesis. Results indicated marginally significant effects of harsh parenting on externalizing problems at age 2. Surprisingly, harsh parenting and exposure to neighborhood risk did not significantly predict increases in externalizing behavior problems from age 2 to 3. Harsh parenting was marginally related to children's internalizing problems under conditions of high levels of neighborhood disadvantage and predicted increases in internalizing over time. The theoretical implications of the results are discussed.

In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.

Given the nature of couple conflict, couples are frequently tasked with mutually working towards a solution to a given problem. Effective conflict management is a key relationship resource that maintains closeness and cohesiveness (Epstein & Baucom, 2002; Overall & Simpson, 2013), whereas ineffective conflict management is generally associated with impaired relationship functioning (Gottman & Notarius, 2000; Overall & Fletcher, 2010). The overarching goal of the present thesis therefore consisted of further advancing the field’s understanding of conflict management as an interpersonal process in heterosexual couple relationships. This goal was addressed through two novel and complementary studies. The sample for both studies consisted of 179 community-based heterosexual adult couples involved in a long-term romantic relationship. During the testing session, participants completed a questionnaire package and discussed a topic of disagreement for 15 minutes. These interactions were then coded for both positive and negative conflict management behaviours. Both studies modeled these conflict management behaviours alongside crucial variables involved in couple conflict. The first study examined the ways in which partners’ adult romantic attachment orientations interact to predict their conflict management behaviours. As hypothesized, the results provide preliminary evidence that, in some cases, men’s and women’s conflict management depends on the interaction between their own and their partner’s adult romantic attachment orientation, more so than a sole individual’s attachment. The second study examined whether effective conflict management moderates the negative association between relationship satisfaction and men’s and women’s use of psychological partner aggression. Contrary to hypotheses, the results suggest that, in predicting these aggressive acts, relationship satisfaction and conflict management make fairly independent contributions. These findings are based on strong theoretical frameworks as well as a number of methodological strengths, including the observational coding of positive and negative conflict management behaviours and the implementation of sophisticated dyadic data analyses. Furthermore, by filling gaps in the existing literature, these findings offer several theoretical, empirical, and clinical implications for the field of couple conflict. As individual and collective bodies of work, the studies of the present thesis provide invaluable evidence in support of the complex and interdependent nature of couple relationships. Such findings are highly relevant to couple researchers and clinicians alike, both of whom endeavour to understand and improve couple relationship functioning.

Because of the huge amounts of data made available by the technology boom in the late twentieth century, new methods are required to turn data into usable information. Much of this data is categorical in nature, which makes estimation difficult in highly multivariate settings. In this thesis we review various multivariate statistical methods, discuss various statistical methods of natural language processing (NLP), and discuss a general class of models described by Erosheva (2002) called generalized mixed membership models. We then propose extensions of the information partition function (IPF) derived by Engler (2002), Oliphant (2003), and Tolley (2006) that will allow modeling of discrete, highly multivariate data in linear models. We report results of the modified IPF model on the World Health Organization's Survey on Global Aging (SAGE).

Alleles, genotypes and haplotypes (combinations of alleles) have been widely used in gene-disease association studies. More recently, association studies using diplotypes (haplotype pairs on homologous chromosomes) have become increasingly common. This article reviews the rationale of the four types of association analyses and discusses the situations in which diplotype-based analyses are more powerful than the other types of association analyses. Haplotype-based association analyses are more powerful than allele-based association analyses, and diplotype-based association analyses are more powerful than genotype-based analyses. In circumstances where there are no interaction effects between markers and where the criteria for Hardy-Weinberg Equilibrium (HWE) are met, the larger sample size and smaller degrees of freedom of allele-based and haplotype-based association analyses make them more powerful than genotype-based and diplotype-based association analyses, respectively. However, under certain circumstances diplotype-based analyses are more powerful than haplotype-based analysis.