by Vincent Hung Hin Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 76-77). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter §1.1 --- Classical approaches and their limitations --- p.1 / Chapter §1.2 --- New approach --- p.3 / Chapter Chapter 2 --- Two-dimensional Ordinal Square Tables --- p.5 / Chapter §2.1 --- Model --- p.5 / Chapter §2.2 --- Maximum likelihood estimator --- p.7 / Chapter §2.3 --- Optimization procedure --- p.8 / Chapter §2.4 --- Useful hypotheses --- p.9 / Chapter §2.5 --- Simulation study --- p.11 / Chapter §2.6 --- A real example --- p.18 / Chapter §2.7 --- Comparison of new and classical approaches --- p.22 / Chapter Chapter 3 --- Multi-dimensional Ordinal Tables --- p.25 / Chapter §3.1 --- Partition maximum likelihood estimator --- p.26 / Chapter §3.2 --- Optimization procedure --- p.28 / Chapter §3.3 --- Useful hypotheses --- p.37 / Chapter §3.4 --- Simulation study --- p.39 / Chapter Chapter 4 --- Conclusion --- p.45 / Tables --- p.48 / Appendix --- p.74 / References --- p.77

Cheung Shu Fai. / "August 2000." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (p. 117-123). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

This thesis includes three parts. The overarching theme is how to analyze multilevel structured datasets, particularly in the areas of survey and causal inference. The first part discusses model selection of hierarchical models, in the context of a national political survey. I found that the commonly used model selection criteria based on predictive accuracy, such as cross validation, don't perform very well in the case of political survey and explore the possible causes. The second part centers around a unique data set on the presidential election collected through an online platform. I show that with adequate modeling, meaningful and highly accurate information could be extracted from this highly-biased data set. The third part builds on a formal causal inference framework for group-structured data, such as meta-analysis and multi-site trials. In particular, I develop a Gaussian Process model under this framework and demonstrate additional insights that can be gained compared with traditional parametric models.

Cheung Wai-leung. / "July 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 110-129). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Lam Yuk Hing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 67-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Diagnostic Measure --- p.7 / Chapter 2.1 --- A diagnostic measure di based on Cook's likelihood distance --- p.7 / Chapter 2.2 --- The estimates 6 and θ (i) --- p.8 / Chapter 2.3 --- "The one-step estimator,θ1 (i)" --- p.9 / Chapter 3 --- Methods For Identifying Influential Observations --- p.11 / Chapter 3.1 --- One-step method --- p.11 / Chapter 3.2 --- Forward search procedure --- p.13 / Chapter 3.2.1 --- Idea of forward search procedure --- p.14 / Chapter 3.2.2 --- "The modified diagnostic measure, di" --- p.15 / Chapter 3.2.3 --- Initial basic subset --- p.18 / Chapter 3.2.4 --- The algorithm of starting with an ordered basic subset --- p.19 / Chapter 3.2.5 --- The algorithm of starting with a random basic subset --- p.21 / Chapter 4 --- Case Study --- p.23 / Chapter 4.1 --- Open/Close Book data set --- p.23 / Chapter 4.1.1 --- One-step method --- p.27 / Chapter 4.1.2 --- Forward search procedure --- p.28 / Chapter 4.1.3 --- Start with the ordered basic subset --- p.28 / Chapter 4.1.4 --- Start with a random basic subset --- p.32 / Chapter 4.2 --- Paper-Quality Measurements data set --- p.38 / Chapter 4.2.1 --- One-step method --- p.40 / Chapter 4.2.2 --- Forward search procedure --- p.41 / Chapter 4.2.3 --- Start with the ordered basic subset --- p.41 / Chapter 4.2.4 --- Start with a random basic subset --- p.45 / Chapter 5 --- Simulation --- p.52 / Chapter 5.1 --- Simulation procedure --- p.52 / Chapter 5.2 --- Results --- p.56 / Chapter 6 --- Discussion --- p.64 / Reference --- p.67

Lau Suk Yin Rebecca. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 111-117). / Abstracts in English and Chinese. / ABSTRACT (ENGLISH) --- p.i / ABSTRACT (CHINESE) --- p.iii / ACKNOWLEDGEMENT --- p.iv / TABLE OF CONTENTS --- p.v / LIST OF TABLES --- p.viii / LIST OF FIGURES --- p.ix / Chapter CHAPTER I --- INTRODUCTION --- p.1 / Chapter CHAPTER II --- LITERATURE REVIEW --- p.6 / Chapter 2.1 --- Definition of Mediating Effects --- p.6 / Chapter 2.2 --- Approaches to Mediational Analyses --- p.12 / Chapter 2.2.1 --- Correlation Approach --- p.13 / Chapter 2.2.2 --- Hierarchical Regression Approach --- p.17 / Chapter 2.2.3 --- SEM Approach --- p.39 / Chapter 2.3 --- Summary --- p.44 / Chapter CHAPTER III --- A TEST FOR THE SIGNIFICANCE OF MEDIATING EFFECTS IN SEM --- p.47 / Chapter 3.1 --- A Significance Test for the Mediating Effects with SEM --- p.48 / Chapter 3.1.1 --- Model without Mediating Effects --- p.48 / Chapter 3.1.2 --- Model with Full Mediation --- p.49 / Chapter 3.1.3 --- Model with Partial Mediation --- p.49 / Chapter 3.1.4 --- Model with Suppression --- p.50 / Chapter 3.2 --- Procedure for Testing the Significance of Mediating Effects in SEM --- p.50 / Chapter 3.3 --- Summary --- p.56 / Chapter CHAPTER IV --- MODEL COMPARISON IN SEM --- p.59 / Chapter 4.2 --- Testing the Significance of Mediating Effects with ΔFIs --- p.61 / Chapter CHAPTER V --- METHODOLOGY OF SIMULATION --- p.65 / Chapter 5.1 --- Resampling Space Generation --- p.65 / Chapter 5.2 --- Sample Generation and Method of Analysis --- p.67 / Chapter CHAPTER VI --- SIMULATION RESULTS AND DISCUSSION --- p.73 / Chapter 6.1 --- Simulation Results --- p.73 / Chapter 6.1.1 --- Variance Explained by Model Characteristics --- p.73 / Chapter 6.1.1.1 --- Variance Explained Under the Condition of No Mediation --- p.80 / Chapter 6.1.1.2 --- Variance Explained Under the Condition of Mediating Effects at 0.1 --- p.81 / Chapter 6.1.1.2.1 --- Variance Explained by Factor Loadings --- p.81 / Chapter 6.1.1.2.2 --- Variance Explained by Sample Size --- p.82 / Chapter 6.1.1.2.3 --- Variance Explained by Number of Items --- p.83 / Chapter 6.1.1.2.4 --- "Variance Explained by 2-Way Interactions of Factor Loadings, Sample Size and Number of Items" --- p.83 / Chapter 6.1.2 --- Correlation between FIs and ΔFIs --- p.84 / Chapter 6.2 --- Simulation Result Discussion --- p.88 / Chapter CHAPTER VII --- NUMERICAL EXAMPLE --- p.91 / Chapter 7.1 --- Testing Mediating Effects in a Model in Past Literature --- p.91 / Chapter 7.2 --- Summary --- p.94 / Chapter CHAPTER VIII --- DISCUSSION --- p.96 / Chapter 8.1 --- Limitations and Directions for Future Research --- p.101 / APPENDIX / Chapter APPENDIX I --- Syntax for Testing the Significance of Mediating Effects (Unconstrained Model) / Chapter APPENDIX II --- Syntax for Testing the Significance of Mediating Effects (Constrained Model) / Chapter APPENDIX III --- Syntax for Testing Full Mediation --- p.106 / Chapter APPENDIX IV --- "Syntax for Testing Mediating Effects in Model by Foley, Kidder & Powell (2002) (DV: Intentions to Leave) (Unconstrained Model)" --- p.107 / Chapter APPENDIX V --- "Syntax for Testing Mediating Effects in Model by Foley, Kidder & Powell (2002) (DV: Intentions to Leave) (Constrained Model)" --- p.108 / Chapter APPENDIX VI --- "Syntax for Testing Mediating Effects in Model by Foley, Kidder & Powell (2002) (DV: Perceived Career Prospects) (Unconstrained Model)" --- p.109 / Chapter APPENDIX VII --- "Syntax for Testing Mediating Effects in Model by Foley, Kidder & Powell (2002) (DV: Perceived Career Prospects) (Constrained Model)" --- p.110 / REFERENCES --- p.111

by Jian-Qing Shi. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1996. / Includes bibliographical references (leaves 118-123). / Chapter Chapter 1. --- Introduction and overview --- p.1 / Chapter Chapter 2. --- General methodology --- p.8 / Chapter Chapter 3. --- A Bayesian approach to confirmatory factor analysis --- p.16 / Chapter 3.1 --- Confirmatory factor analysis model and its prior --- p.16 / Chapter 3.2 --- The algorithm of data augmentation --- p.19 / Chapter 3.2.1 --- Data augmentation and one-run method --- p.19 / Chapter 3.2.2 --- Rao-Blackwellized estimation --- p.22 / Chapter 3.3 --- Asymptotic properties --- p.28 / Chapter 3.3.1 --- Asymptotic normality and posterior covariance matrix --- p.28 / Chapter 3.3.2 --- Goodness-of-fit statistic --- p.31 / Chapter Chapter 4. --- Bayesian inference for structural equation models --- p.34 / Chapter 4.1 --- LISREL Model and prior information --- p.34 / Chapter 4.2 --- Algorithm and conditional distributions --- p.38 / Chapter 4.2.1 --- Data augmentation algorithm --- p.38 / Chapter 4.2.2 --- Conditional distributions --- p.39 / Chapter 4.3 --- Posterior analysis --- p.44 / Chapter 4.3.1 --- Rao-Blackwellized estimation --- p.44 / Chapter 4.3.2 --- Asymptotic properties and goodness-of-fit statistic --- p.45 / Chapter 4.4 --- Simulation study --- p.47 / Chapter Chapter 5. --- A Bayesian estimation of factor score with non-standard data --- p.52 / Chapter 5.1 --- General Bayesian approach to polytomous data --- p.52 / Chapter 5.2 --- Covariance matrix of the posterior distribution --- p.61 / Chapter 5.3 --- Data augmentation --- p.65 / Chapter 5.4 --- EM algorithm --- p.68 / Chapter 5.5 --- Analysis of censored data --- p.72 / Chapter 5.5.1 --- General Bayesian approach --- p.72 / Chapter 5.5.2 --- EM algorithm --- p.76 / Chapter 5.6 --- Analysis of truncated data --- p.78 / Chapter Chapter 6. --- Structural equation model with continuous and polytomous data --- p.82 / Chapter 6.1 --- Factor analysis model with continuous and polytomous data --- p.83 / Chapter 6.1.1 --- Model and Bayesian inference --- p.83 / Chapter 6.1.2 --- Gibbs sampler algorithm --- p.85 / Chapter 6.1.3 --- Thresholds parameters --- p.89 / Chapter 6.1.4 --- Posterior analysis --- p.92 / Chapter 6.2 --- LISREL model with continuous and polytomous data --- p.94 / Chapter 6.2.1 --- LISREL model and Bayesian inference --- p.94 / Chapter 6.2.2 --- Posterior analysis --- p.101 / Chapter 6.3 --- Simulation study --- p.103 / Chapter Chapter 7. --- Further development --- p.108 / Chapter 7.1 --- More about one-run method --- p.108 / Chapter 7.2 --- Structural equation model with censored data --- p.111 / Chapter 7.3 --- Multilevel structural equation model --- p.114 / References --- p.118 / Appendix --- p.124 / Chapter A.1 --- The derivation of conditional distribution --- p.124 / Chapter A.2 --- Generate a random variate from normal density which restricted in an interval --- p.129 / Tables --- p.132 / Figures --- p.155

Oi-Ping Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 45-46). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Model --- p.4 / Chapter 2.1 --- General Model --- p.4 / Chapter 2.2 --- Constraint --- p.5 / Chapter 3 --- Bayesian Estimation via Gibbs Sampler --- p.7 / Chapter 3.1 --- Conditional Distributions --- p.10 / Chapter 3.2 --- Constraint --- p.15 / Chapter 3.3 --- Bayesian Estimation --- p.16 / Chapter 4 --- Model Comparison using the Bayes Factor --- p.18 / Chapter 5 --- Simulation Study --- p.22 / Chapter 6 --- Real Example --- p.27 / Chapter 6.1 --- Model Selection --- p.29 / Chapter 6.2 --- Bayesian Estimate --- p.30 / Chapter 6.3 --- Sensitivity Analysis --- p.31 / Chapter 7 --- Discussion --- p.32 / Chapter A --- p.34 / Bibliography --- p.45

Cheng, Tsz Ho Tony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 49-68). / Abstract also in Chinese. / ABSTRACT --- p.i / 摘要 --- p.ii / ACKNOWLEDGEMENT --- p.iii / LIST OF TABLES --- p.vi / Chapter CHAPTER 1. --- INTRODUCTION --- p.1 / What is El? --- p.2 / Controversies of El --- p.5 / Previous Research of El in the Workplace --- p.6 / Purpose of the Present Meta-Analysis --- p.7 / Specific Hypotheses Concerning El-Workplace Criterion Relations --- p.8 / El and Job Performance --- p.8 / El and Job-Related Attitudes --- p.9 / El and Leadership --- p.12 / El Models as Moderator --- p.15 / Emotional Labor Demand as Moderator --- p.16 / Gender as Moderator --- p.18 / Source of Criterion Ratings --- p.19 / Publication Bias --- p.20 / Chapter CHAPTER 2. --- METHOD --- p.21 / Literature Search --- p.21 / Inclusion Criteria --- p.21 / Coding --- p.22 / Meta-Analytic Calculation --- p.23 / Chapter CHAPTER 3. --- RESULTS --- p.26 / Descriptive Statistics of Included Samples --- p.26 / Reliability Estimates for Emotional Intelligence and Its Correlates --- p.27 / Mean Effect Analyses --- p.27 / Moderator Analyses --- p.28 / Chapter CHAPTER 4. --- DISCUSSION --- p.38 / Discussion of Key Findings --- p.38 / Main Effects of El --- p.38 / El Models --- p.39 / Emotional Labor Demand --- p.41 / Gender --- p.42 / Source of Criterion Ratings --- p.43 / Future Research --- p.44 / Conceptualizing and Measuring El --- p.44 / El at Team Level --- p.45 / Training Programs --- p.46 / Limitations --- p.46 / Concluding Remarks --- p.48 / REFERENCES --- p.49 / APPENDIX A --- p.69

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