{"product_id":"structural-equation-modeling-9780470024232","title":"Structural Equation Modeling","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eWinner of the 2008 Ziegel Prize for outstanding new book of the year   Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"This book is a welcome addition to any library and should be a valuable resource for research and teaching.\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, August 2008)\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eAbout the Author xi\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Standard Structural Equation Models 1\u003c\/p\u003e \u003cp\u003e1.2 Covariance Structure Analysis 2\u003c\/p\u003e \u003cp\u003e1.3 Why a New Book? 3\u003c\/p\u003e \u003cp\u003e1.4 Objectives of the Book 4\u003c\/p\u003e \u003cp\u003e1.5 Data Sets and Notations 6\u003c\/p\u003e \u003cp\u003eAppendix 1.1 7\u003c\/p\u003e \u003cp\u003eReferences 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Some Basic Structural Equation Models 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 13\u003c\/p\u003e \u003cp\u003e2.2 Exploratory Factor Analysis 15\u003c\/p\u003e \u003cp\u003e2.3 Confirmatory and Higher-order Factor Analysis Models 18\u003c\/p\u003e \u003cp\u003e2.4 The LISREL Model 22\u003c\/p\u003e \u003cp\u003e2.5 The Bentler–Weeks Model 26\u003c\/p\u003e \u003cp\u003e2.6 Discussion 27\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Covariance Structure Analysis 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 31\u003c\/p\u003e \u003cp\u003e3.2 Definitions, Notations and Preliminary Results 33\u003c\/p\u003e \u003cp\u003e3.3 GLS Analysis of Covariance Structure 36\u003c\/p\u003e \u003cp\u003e3.4 ml Analysis of Covariance Structure 41\u003c\/p\u003e \u003cp\u003e3.5 Asymptotically Distribution-free Methods 44\u003c\/p\u003e \u003cp\u003e3.6 Some Iterative Procedures 47\u003c\/p\u003e \u003cp\u003eAppendix 3.1: Matrix Calculus 53\u003c\/p\u003e \u003cp\u003eAppendix 3.2: Some Basic Results in Probability Theory 57\u003c\/p\u003e \u003cp\u003eAppendix 3.3: Proofs of Some Results 59\u003c\/p\u003e \u003cp\u003eReferences 65\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Bayesian Estimation of Structural Equation Models 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 67\u003c\/p\u003e \u003cp\u003e4.2 Basic Principles and Concepts of Bayesian Analysis of SEMs 70\u003c\/p\u003e \u003cp\u003e4.3 Bayesian Estimation of the CFA Model 81\u003c\/p\u003e \u003cp\u003e4.4 Bayesian Estimation of Standard SEMs 95\u003c\/p\u003e \u003cp\u003e4.5 Bayesian Estimation via WinBUGS 98\u003c\/p\u003e \u003cp\u003eAppendix 4.1: The Metropolis–Hastings Algorithm 104\u003c\/p\u003e \u003cp\u003eAppendix 4.2: EPSR Value 105\u003c\/p\u003e \u003cp\u003eAppendix 4.3: Derivations of Conditional Distributions 106\u003c\/p\u003e \u003cp\u003eReferences 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Model Comparison and Model Checking 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 111\u003c\/p\u003e \u003cp\u003e5.2 Bayes Factor 113\u003c\/p\u003e \u003cp\u003e5.3 Path Sampling 115\u003c\/p\u003e \u003cp\u003e5.4 An Application: Bayesian Analysis of SEMs with Fixed Covariates 120\u003c\/p\u003e \u003cp\u003e5.5 Other Methods 127\u003c\/p\u003e \u003cp\u003e5.6 Discussion 130\u003c\/p\u003e \u003cp\u003eAppendix 5.1: Another Proof of Equation (5.10) 131\u003c\/p\u003e \u003cp\u003eAppendix 5.2: Conditional Distributions for Simulating (θ, ΩlY, t) 133\u003c\/p\u003e \u003cp\u003eAppendix 5.3: PP p-values for Model Assessment 136\u003c\/p\u003e \u003cp\u003eReferences 136\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Structural Equation Models with Continuous and Ordered Categorical Variables 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 139\u003c\/p\u003e \u003cp\u003e6.2 The Basic Model 142\u003c\/p\u003e \u003cp\u003e6.3 Bayesian Estimation and Goodness-of-fit 144\u003c\/p\u003e \u003cp\u003e6.4 Bayesian Model Comparison 155\u003c\/p\u003e \u003cp\u003e6.5 Application 1: Bayesian Selection of the Number of Factors in EFA 159\u003c\/p\u003e \u003cp\u003e6.6 Application 2: Bayesian Analysis of Quality of Life Data 164\u003c\/p\u003e \u003cp\u003eReferences 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Structural Equation Models with Dichotomous Variables 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 175\u003c\/p\u003e \u003cp\u003e7.2 Bayesian Analysis 177\u003c\/p\u003e \u003cp\u003e7.3 Analysis of a Multivariate Probit Confirmatory Factor Analysis Model 186\u003c\/p\u003e \u003cp\u003e7.4 Discussion 190\u003c\/p\u003e \u003cp\u003eAppendix 7.1: Questions Associated with the Manifest Variables 191\u003c\/p\u003e \u003cp\u003eReferences 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Nonlinear Structural Equation Models 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 195\u003c\/p\u003e \u003cp\u003e8.2 Bayesian Analysis of a Nonlinear SEM 197\u003c\/p\u003e \u003cp\u003e8.3 Bayesian Estimation of Nonlinear SEMs with Mixed Continuous and Ordered Categorical Variables 215\u003c\/p\u003e \u003cp\u003e8.4 Bayesian Estimation of SEMs with Nonlinear Covariates and Latent Variables 220\u003c\/p\u003e \u003cp\u003e8.5 Bayesian Model Comparison 230\u003c\/p\u003e \u003cp\u003eReferences 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Two-level Nonlinear Structural Equation Models 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 243\u003c\/p\u003e \u003cp\u003e9.2 A Two-level Nonlinear SEM with Mixed Type Variables 244\u003c\/p\u003e \u003cp\u003e9.3 Bayesian Estimation 247\u003c\/p\u003e \u003cp\u003e9.4 Goodness-of-fit and Model Comparison 255\u003c\/p\u003e \u003cp\u003e9.5 An Application: Filipina CSWs Study 259\u003c\/p\u003e \u003cp\u003e9.6 Two-level Nonlinear SEMs with Cross-level Effects 267\u003c\/p\u003e \u003cp\u003e9.7 Analysis of Two-level Nonlinear SEMs using WinBUGS 275\u003c\/p\u003e \u003cp\u003eAppendix 9.1: Conditional Distributions: Two-level Nonlinear Sem 279\u003c\/p\u003e \u003cp\u003eAppendix 9.2: MH Algorithm: Two-level Nonlinear SEM 283\u003c\/p\u003e \u003cp\u003eAppendix 9.3: PP p-value for Two-level NSEM with Mixed Continuous and Ordered-categorical Variables 285\u003c\/p\u003e \u003cp\u003eAppendix 9.4: Questions Associated with the Manifest Variables 286\u003c\/p\u003e \u003cp\u003eAppendix 9.5: Conditional Distributions: SEMs with Cross-level Effects 286\u003c\/p\u003e \u003cp\u003eAppendix 9.6: The MH algorithm: SEMs with Cross-level Effects 289\u003c\/p\u003e \u003cp\u003eReferences 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Multisample Analysis of Structural Equation Models 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 293\u003c\/p\u003e \u003cp\u003e10.2 The Multisample Nonlinear Structural Equation Model 294\u003c\/p\u003e \u003cp\u003e10.3 Bayesian Analysis of Multisample Nonlinear SEMs 297\u003c\/p\u003e \u003cp\u003e10.4 Numerical Illustrations 302\u003c\/p\u003e \u003cp\u003eAppendix 10.1: Conditional Distributions: Multisample SEMs 313\u003c\/p\u003e \u003cp\u003eReferences 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Finite Mixtures in Structural Equation Models 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 319\u003c\/p\u003e \u003cp\u003e11.2 Finite Mixtures in SEMs 321\u003c\/p\u003e \u003cp\u003e11.3 Bayesian Estimation and Classification 323\u003c\/p\u003e \u003cp\u003e11.4 Examples and Simulation Study 330\u003c\/p\u003e \u003cp\u003e11.5 Bayesian Model Comparison of Mixture SEMs 344\u003c\/p\u003e \u003cp\u003eAppendix 11.1: The Permutation Sampler 351\u003c\/p\u003e \u003cp\u003eAppendix 11.2: Searching for Identifiability Constraints 352\u003c\/p\u003e \u003cp\u003eReferences 352\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Structural Equation Models with Missing Data 355\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 355\u003c\/p\u003e \u003cp\u003e12.2 A General Framework for SEMs with Missing Data that are Mar 357\u003c\/p\u003e \u003cp\u003e12.3 Nonlinear SEM with Missing Continuous and Ordered Categorical Data 359\u003c\/p\u003e \u003cp\u003e12.4 Mixture of SEMs with Missing Data 370\u003c\/p\u003e \u003cp\u003e12.5 Nonlinear SEMs with Nonignorable Missing Data 375\u003c\/p\u003e \u003cp\u003e12.6 Analysis of SEMs with Missing Data via WinBUGS 386\u003c\/p\u003e \u003cp\u003eAppendix 12.1: Implementation of the MH Algorithm 389\u003c\/p\u003e \u003cp\u003eReferences 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Structural Equation Models with Exponential Family of Distributions 393\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 393\u003c\/p\u003e \u003cp\u003e13.2 The SEM Framework with Exponential Family of Distributions 394\u003c\/p\u003e \u003cp\u003e13.3 A Bayesian Approach 398\u003c\/p\u003e \u003cp\u003e13.4 A Simulation Study 402\u003c\/p\u003e \u003cp\u003e13.5 A Real Example: A Compliance Study of Patients 404\u003c\/p\u003e \u003cp\u003e13.6 Bayesian Analysis of an Artificial Example using WinBUGS 411\u003c\/p\u003e \u003cp\u003e13.7 Discussion 416\u003c\/p\u003e \u003cp\u003eAppendix 13.1: Implementation of the MH Algorithms 417\u003c\/p\u003e \u003cp\u003eAppendix 13.2 419\u003c\/p\u003e \u003cp\u003eReferences 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Conclusion 421\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 425\u003c\/p\u003e \u003cp\u003eIndex 427\u003c\/p\u003e","brand":"John Wiley \u0026 Sons 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