| Handbook Series on Computing and Statistics with Applications | p. V |
| Preface | p. VII |
| About the Authors | p. IX |
| Contributors | p. XV |
| Covariance Structure Models for Maximal Reliability of Unit-Weighted Composites | p. 1 |
| Proposed identification condition for factor models | p. 3 |
| Reliability based on proposed parameterization | p. 5 |
| Properties of the coefficient | p. 6 |
| Illustration with exploratory factor analysis | p. 7 |
| Reliability with general latent variable models | p. 8 |
| Dimension-free and greatest lower bound reliability | p. 11 |
| Reliability of weighted composites | p. 12 |
| Selection of weights for maximal reliability | p. 14 |
| Conclusions | p. 15 |
| Acknowledgements | p. 16 |
| p. 16 |
| References | p. 17 |
| Advances in Analysis of Mean and Covariance Structure when Data are Incomplete | p. 21 |
| Introduction | p. 21 |
| Missing data mechanism | p. 24 |
| Methods for handling missing data | p. 26 |
| Simulation studies | p. 34 |
| Sensitivity analysis for missing data mechanism | p. 36 |
| SEM software for incomplete data | p. 41 |
| References | p. 42 |
| Rotation Algorithms: From Beginning to End | p. 45 |
| Introduction | p. 45 |
| Factor analysis | p. 46 |
| A parameterization for [Lambda] and [Phi] | p. 48 |
| Reference structures | p. 49 |
| Thurstone's graphical rotation method | p. 49 |
| Early analytic oblique rotation methods | p. 52 |
| Pairwise algorithms | p. 53 |
| Analytic rotation methods: Orthogonal | p. 54 |
| Direct analytic methods: Oblique | p. 59 |
| Discussion | p. 61 |
| References | p. 63 |
| Selection of Manifest Variables | p. 65 |
| Introduction | p. 65 |
| Manifest variable selection in factor analysis | p. 67 |
| SEFA and examples with empirical data | p. 72 |
| Variable selection with a model fit and reliability analysis | p. 77 |
| Conclusion and final remarks | p. 83 |
| Acknowledgements | p. 84 |
| References | p. 84 |
| Bayesian Analysis of Mixtures Structural Equation Models with Missing | p. 87 |
| Introduction | p. 87 |
| Model description | p. 89 |
| Bayesian analysis of the models | p. 90 |
| Simulation studies | p. 94 |
| An illustrative example | p. 100 |
| Analysis via WinBUGS | p. 102 |
| Discussion | p. 104 |
| Acknowledgements | p. 104 |
| The permutation sampler | p. 105 |
| Searching for identifiability constraints | p. 105 |
| Manifest variables in the ICPSR example | p. 106 |
| References | p. 106 |
| Local Influence Analysis for Latent Variable Models with Non-Ignorable Missing Responses | p. 109 |
| Introduction | p. 109 |
| Local influence of latent variable models with non-ignorable missing data | p. 111 |
| Normal mixed effects model | p. 114 |
| Generalized linear mixed model | p. 123 |
| Conclusion | p. 128 |
| p. 129 |
| p. 129 |
| p. 130 |
| References | p. 133 |
| Goodness-of-Fit Measures for Latent Variable Models for Binary Data | p. 135 |
| Introduction | p. 135 |
| Latent variable models for binary responses | p. 136 |
| Goodness-of-fit tests for latent variable models for binary data | p. 138 |
| Limited information statistics | p. 141 |
| Test based on the log-odds ratio | p. 144 |
| Simulations | p. 146 |
| Conclusion | p. 158 |
| Acknowledgements | p. 160 |
| References | p. 160 |
| Bayesian Structural Equation Modeling | p. 163 |
| Introduction | p. 163 |
| Structural equation models | p. 165 |
| Bayesian approach | p. 167 |
| Democratization and industrialization application | p. 172 |
| Discussion and future research | p. 181 |
| Prior specifications | p. 182 |
| Results: posterior parameters estimates (see Table B.1) | p. 184 |
| References | p. 186 |
| The Analysis of Structural Equation Model with Ranking Data using Mx | p. 189 |
| Introduction | p. 189 |
| Multivariate normal model for analyzing ranking and ordinal categorical data | p. 190 |
| Implementation by Mx | p. 192 |
| Applications | p. 197 |
| Discussion | p. 201 |
| Acknowledgements | p. 202 |
| Mx input script for p = 4, auto data set, basic Thurstonian model | p. 202 |
| Mx input script, auto data set, factor analysis model | p. 204 |
| Mx input script, auto data set, model of reduced form parameters | p. 205 |
| References | p. 206 |
| Multilevel Structural Equation Modeling | p. 209 |
| Introduction | p. 209 |
| Response types | p. 210 |
| Multilevel measurement models | p. 212 |
| Multilevel structural equation models | p. 217 |
| Estimation | p. 219 |
| Application: Student ability and teacher excellence | p. 220 |
| References | p. 226 |
| Statistical Inference of Moment Structures | p. 229 |
| Introduction | p. 229 |
| Moment structures models | p. 229 |
| Minimum discrepancy function estimation approach | p. 234 |
| Consistency of MDF estimators | p. 237 |
| Asymptotic analysis of the MDF estimation procedure | p. 239 |
| Asymptotic robustness of the MDF statistical inference | p. 252 |
| Acknowledgements | p. 258 |
| References | p. 258 |
| Meta-Analysis and Latent Variable Models for Binary Data | p. 261 |
| Introduction | p. 261 |
| Meta-analysis for binary data | p. 263 |
| Publication bias and sensitivity analysis | p. 267 |
| An illustrated example | p. 271 |
| Discussion and further development | p. 275 |
| References | p. 277 |
| Analysis of Multisample Structural Equation Models with Applications to Quality of Life Data | p. 279 |
| Introduction | p. 279 |
| A multisample SEM with missing ordered categorical variables | p. 281 |
| ML analysis | p. 283 |
| Illustrative example: analysis of multisample synthetic QOL data | p. 288 |
| Discussion | p. 297 |
| Acknowledgements | p. 298 |
| p. 298 |
| p. 300 |
| References | p. 300 |
| The Set of Feasible Solutions for Reliability and Factor Analysis | p. 303 |
| Introduction | p. 304 |
| The Ledermann bound | p. 306 |
| Reliability theory and a convex set of possible solutions for (2) | p. 307 |
| Minimizing the sum and the sum of squares of unexplained common variances | p. 310 |
| The feasible set from two perspectives | p. 312 |
| Reliability measures derived from a single factor solution | p. 313 |
| Reliability derived from multiple factor analysis | p. 315 |
| Discussion | p. 318 |
| References | p. 319 |
| Nonlinear Structural Equation Modeling as a Statistical Method | p. 321 |
| Introduction | p. 321 |
| General nonlinear structural equation model | p. 322 |
| Pseudo-likelihood estimation for the general nonlinear structural equation model | p. 327 |
| Example | p. 332 |
| Discussion | p. 338 |
| p. 339 |
| References | p. 341 |
| Matrix Methods and their Applications to Factor Analysis | p. 345 |
| Introduction | p. 345 |
| Fundamentals of matrix methods | p. 346 |
| Applications of matrix methods to factor analysis | p. 350 |
| Acknowledgements | p. 365 |
| References | p. 365 |
| Robust Procedures in Structural Equation Modeling | p. 367 |
| Introduction | p. 367 |
| Nonnal theory ML and related procedures | p. 370 |
| Generalized Least Squares (GLS) procedures | p. 374 |
| Real robust procedures | p. 377 |
| Misspecified models | p. 385 |
| Illustration | p. 388 |
| References | p. 393 |
| Stochastic Approximation Algorithms for Estimation of Spatial Mixed Models | p. 399 |
| Introduction | p. 399 |
| Spatial mixed models | p. 401 |
| Estimation procedure | p. 403 |
| Applications | p. 411 |
| Acknowledgements | p. 418 |
| References | p. 418 |
| Author Index | p. 423 |
| Subject Index | p. 431 |
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