行为学、社会学、心理学和医药学方面，结构方程模型(SEMs) 是研究有关潜在变量最常用的模型。这篇论文的目的是研究基本和高级结构方程模型的贝叶斯诊断，本文研究的结构方程模型包括非线性纺构方程模型、变换结构方程模型、二层结构方程模型和混合结构方程模型。基于对数贝叶斯因子的一阶与二阶局部影响测度是本文进行贝贝叶斯诊断的基础。局部影响测度的计算和模型参数估计是利用了蒙特卡洛(MCMC) 和扩展数据的方法。对比传统的基于极大似然的诊断，本文提出的贝叶斯诊断方法不仅能检测异常点或者影响点，而且可以诊断模型假设和先验设定的敏感性。 这些是通过对数据、模型假设和先验设定进行不同的扰动获得的 本文用大量的模拟实验来说明所提出的贝叶斯诊断方法的作用。 本文基于不同类型的结构方程模型，应用所提出的贝叶斯诊断方法于一些实际数据。 / In the behavioral, social, psychological, and medical sciences, the most widely used models in assessing latent variables are structural equation models (SEMs). This thesis aims to develop Bayesian diagnostic procedures for basic and advanced SEMs such as nonlinear SEMs, transformation SEMs, two-level SEMs, and mixture SEMs. The first- and second-order local inference measures with the objective functions defined based on the logarithm of Bayes factor are proposed to perform the Bayesian diagnostics. Markov chain Monte Carlo (MCMC) methods, along with data augmentation, are developed to compute the local influence measures and to estimate unknown model parameters. Compared with conventional maximum likelihood-based diagnostic procedures, the proposed Bayesian diagnostic approach can not only detect outliers or influential points in the observed data, but also conduct model comparison and sensitivity analysis by perturbing the data, sampling distributions, and the prior distributions of model parameters via a variety of perturbations. The empirical performances of the proposed Bayesian diagnostic procedures are revealed through extensive simulation studies. Several real-life data sets are used to illustrate the application of our proposed methodology in the context of different SEMs. / Detailed summary in vernacular field only. / Chen, Ji. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 130-135). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Structural equation models --- p.1 / Chapter 1.2 --- Bayesian diagnostics --- p.3 / Chapter 1.2.1 --- The first and second order local influence measures --- p.5 / Chapter 1.2.2 --- A simple example --- p.9 / Chapter 2 --- Bayesian diagnostics of nonlinear SEMs --- p.15 / Chapter 2.1 --- Model description --- p.16 / Chapter 2.2 --- Bayesian estimation and local inference of nonlinear SEMs --- p.17 / Chapter 2.3 --- Simulation study --- p.24 / Chapter 2.3.1 --- Simulation study 1 --- p.24 / Chapter 2.3.2 --- Simulation study 2 --- p.25 / Chapter 2.3.3 --- Simulation study 3 --- p.27 / Chapter 2.4 --- Application: A study of kidney disease for type 2 diabetic patients --- p.29 / Chapter 3 --- Bayesian diagnostics of transformation SEMs --- p.40 / Chapter 3.1 --- Model description --- p.41 / Chapter 3.2 --- Bayesian estimation and local inference of the transformation SEMs --- p.44 / Chapter 3.3 --- Simulation study --- p.54 / Chapter 3.3.1 --- Simulation study 1 --- p.54 / Chapter 3.3.2 --- Simulation study 2 --- p.56 / Chapter 3.4 --- Application: A study on the risk factors of osteoporotic fracture in older people --- p.58 / Chapter 4 --- Bayesian diagnostics of two-level SEMs --- p.73 / Chapter 4.1 --- Model description --- p.74 / Chapter 4.2 --- Bayesian estimation and local inference of two-level SEMs --- p.75 / Chapter 4.3 --- Simulation study --- p.88 / Chapter 4.4 --- Application: A study of AIDS data --- p.91 / Chapter 5 --- Bayesian diagnostics of mixture SEMs --- p.106 / Chapter 5.1 --- Model description --- p.107 / Chapter 5.2 --- Bayesian estimation and local inference ofmixture SEMs --- p.108 / Chapter 5.3 --- Simulation study --- p.116 / Chapter 5.3.1 --- Simulation study 1 --- p.116 / Chapter 5.3.2 --- Simulation study 2 --- p.118 / Chapter 6 --- Conclusion --- p.126 / Bibliography --- p.130 / Chapter A --- Proof of Theorem 1.1 and 1.2 --- p.136 / Chapter B --- Full conditional distributions of the nonlinear SEM --- p.138 / Chapter C --- Full conditional distributions of the transformation SEM --- p.141 / Chapter D --- Full conditional distributions of the two-level SEM --- p.144 / Chapter E --- AIDS preventative intervention data --- p.150 / Chapter F --- Permutation sampler in the mixture SEM --- p.152 / Chapter G --- Full conditional distributions of the mixture SEM --- p.153

Leung Shek-hay. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 53-55). / Abstracts in English and Chinese. / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Finite Mixtures of Structural Equation Model --- p.4 / Chapter Chapter 3 --- Bayesian Approach --- p.7 / Chapter Chapter 4 --- Two-stage Approach --- p.16 / Chapter Chapter 5 --- Simualtion Study --- p.22 / Chapter 5.1 --- Performance of the Two Approaches --- p.22 / Chapter 5.2 --- Influence of Prior Information of the Two Approaches --- p.26 / Chapter 5.3 --- Influence of the Component Probability to the Two Approaches --- p.28 / Chapter 5.4 --- Performance of the Two Approaches when the Components are not well-separated --- p.29 / Chapter Chapter 6 --- A Real Data Analysis --- p.31 / Chapter Chapter 7 --- Conclusion and Discussion --- p.35 / Appendix A Derviation of the Conditional Distribution --- p.37 / Appendix B Manifest Variables in the ICPSR Example --- p.39 / Appendix C A Sample LISREL Program for a Classified Group in the Simualtion Study --- p.40 / Appendix D A Sample LISREL Program for a Classified Group in the ICPSR Example --- p.41 / Tables 1-9 --- p.42 / Figures 1-2 --- p.51 / References --- p.53

Wang, Chang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 139-145). / Abstracts in English and Chinese. / ABSTRACT --- p.3 / CHINESE ABSTRACT --- p.5 / ACKNOWLEDGMENTS --- p.6 / TABLE OF CONTENTS --- p.7 / LIST OF TABLES --- p.9 / LIST OF FIGURES --- p.10 / INTRODUCTION --- p.11 / Chapter 1.1 --- ISSUE OF MODEL FIT IN SEM --- p.11 / Chapter 1.2 --- CLASSIFICATION AND DEVELOPMENT OF FIT INDICES --- p.13 / Chapter 1.3 --- ORGANIZATION OF THIS THESIS --- p.18 / Chapter CHAPTER 2 --- ISSUES OF FIT INDICES IN ASSESSING MODEL FIT --- p.19 / Chapter 2.1 --- SENSITIVITY OF FIS TO MODL PARAMETER --- p.19 / Chapter 2.1.1 --- Sample size --- p.20 / Chapter 2.1.2 --- Model complexity --- p.21 / Chapter 2.1.3 --- Misspecification --- p.23 / Chapter 2.2 --- MEASUREMENT ERROR --- p.26 / Chapter 2.3 --- PERFECT FIT VS. APPROXIMATE FIT --- p.26 / Chapter 2.4 --- Minimum Fit Function chi-square vs. Normal-theory Weighted Least chi-square --- p.29 / Chapter 2.5 --- RULE OF THUMB --- p.30 / Chapter 2.6 --- FIVE RESEARCH QUESTIONS --- p.37 / Chapter CHAPTER 3 --- SIMULATION --- p.39 / Chapter 3.1 --- FIT INDICES --- p.39 / Chapter 3.2 --- DESIGN OF MONTE CARLO SIMULATIONS --- p.38 / Chapter 3.3 --- MODEL COMPLEXITY AND MODEL SPECIFICATION --- p.39 / Chapter 3.4 --- SIMULATION PROCEDURE --- p.41 / Chapter CHAPTER 4 --- RESULTS --- p.45 / Chapter 4.1 --- MEASUREMENT ERROR AND CRONBACH´ةS ALPHA --- p.45 / Chapter 4.2 --- ANSWER TO Q1 --- p.45 / Chapter 4.3 --- ANSWER TO Q2 --- p.53 / Chapter 4.4 --- ANSWER TO Q3 --- p.56 / Chapter 4.5 --- ANSWER TO Q4 --- p.60 / Chapter 4.6 --- ANSWER TO Q5 --- p.62 / Chapter CHAPTER 5 --- DUSCUSSION --- p.77 / Chapter 5.1 --- DUSCUSSION TO Q1 --- p.77 / Chapter 5.2 --- DUSCUSSION TO Q2 --- p.83 / Chapter 5.3 --- DUSCUSSION TO Q3 --- p.85 / Chapter 5.4 --- DUSCUSSION TO Q4 --- p.88 / Chapter 5.5 --- DUSCUSSION TO Q5 --- p.89 / Chapter CHAPTER 6 --- LIMITATION --- p.99 / Chapter CHAPTER 7 --- CONCLUSION --- p.101 / PREFERENCE --- p.139

Structural Equations Models (SEMs) are now widely used almost in every discipline of research. Most of the existing materials for the Namibian macroeconomic models are studies of the well documented time series approach. In this study, we provided a statistical approach on modelling the Namibian macroeconomics for the real and fiscal economic sectors using SEMs. The approach is based on testing the theoretical specification laid down by the Namibian Macroeconometrics Model (NAMEX) of 2004. The economic structure and relationships among the variables is evaluated by means of exploratory and confirmatory analysis and the results are congruent to the existing theory in terms of loading patterns. Between Maximum Likelihood (ML) and Generalized Least Square (GLS) estimation methods, we compared the discrepancy of parameter estimates under the commonly encountered problems of sample size, violation of underlying assumptions in the data as well as model misspecifications. GLS estimation methods seem to provide better goodness of fit indices under those conditions. We have also shown that the fiscal sector is not well represented by our SEM. We recommend further studies to employ sufficiently larger samples so that models are correctly specified.

Literature reports that test anxiety may have an influence on aptitude test performance for some racial groups and therefore serves as a source of bias (Zeidner, 1998). Testing organisations have also found that individuals from African groups perform poorly on measures of fluid intelligence, putting them at a disadvantage when these scores are used for selection and training purposes. The current study examines a model defining the relationship between test anxiety, attentional resources and fluid intelligence in the following manner: an increase in test anxiety will result in a decrease of attentional resources as well as a decrease in fluid intelligence. With a decrease in attentional resources we will see a negative influence on fluid intelligence and test performance for different racial groups. Twenty-five African individuals and twenty-five individuals from Caucasian racial groups have set the stage to answer the question if certain groups experience higher test anxiety and thus perform poorly on fluid intelligence measures. Significant relationships were found, within and between groups, for attentional resources and fluid intelligence. Meanwhile, other factors, such as test anxiety, were not strongly associated with fluid intelligence performance. Future research into reasons why certain racial groups display lower overall attention in testing situations is suggested in order to ensure that tests for selection and training and aptitude tests are fair to all racial groups. / Psychology / M.A. Soc. Sc.(Psychology)

This study attempted to determine the specific mathematics necessary to a student in a beginning course in behavioral science research statistics. To determine the most desirable form for a review of mathematics prior to a research statistics course,, it was first necessary to determine the following: (1) the specific overall content of such a course, (2) the specific mathematics topics of such a course, and (3) the specific mathematics operations utilized in such a course. The study consisted of three parts. The first phase was a determination of the content of a typical beginning course in research statistics for the behavioral sciences. To make this determination, a survey was conducted among forty universities chosen by random sampling from those in the United States offering the Doctor of Education degree. Course outlines and textbooks used by these universities were analyzed, and topics were tabulated. In addition, a selection of recent statistics texts was analyzed, and these topics were also tabulated. These tables were used as a means of content determination.

Cluster analysis refers to a type of statistical method designed to identify homogeneous groups within complex, multivariate data sets. In this study two newly developed genetic cluster analysis algorithms, GENCLUS and GENCLUS+, were validated by comparing their performance against that of three popular clustering techniques (Ward's method, K-means w/ random seeds, K-means w/Ward's centroids) and in an elaborate Monte Carlo study. Additionally, the ability of GENCLUS+ to determine the correct number of clusters was compared against that of three conventional procedures (Calinski and Harabasz, C-index, trace W). GENCLUS and GENCLUS+ achieved Rand recovery values slightly inferior to those of conventional methods. However, GENCLUS+ appeared to perform better than conventional methods in an empirical analysis, and genetic method solutions appear to possess high internal cohesion and external isolation. The mixed results are interpreted as an indication of a discrepancy between cluster theory and conventional data generation techniques. / Ph. D.