
Handbook of Computing and Statistics with Applications
Volume 1
By: Sik-Yum Lee (Volume Editor)
Hardcover | 1 March 2007
At a Glance
460 Pages
24.13 x 17.15 x 2.54
Hardcover
$458.75
or 4 interest-free payments of $114.69 with
orShips in 10 to 15 business days
About the Authors
1. Covariance St ructure Models for Maximal Reliability of Unit-weighted Composites (Pete r M. Bentler)
2. Advances in Analysis of Mean and Covarianc e Structure When Data are Incomplete (Mortaza Jamshidian, Matthew Mata)< BR id='CRLF'>3. Rotation Algorithms: From Beginning to End (Robert I. Je nnrich)
4. Selection of Manifest Variables (Yutaka Kano)
5. Bayesian Analysis of Mixtures Structural Equation Models w ith Missing Data (Sik-Yum Lee)
6. Local Influence Analysis for Latent Variable Models with Nonignorable Missing Responses (Bin Lu, Xin-Yuan Song, Sik-Yum Lee, Fernand Mac-Moune Lai)
7. Goodn ess-of-fit Measures for Latent Variable Models for Binary Data (D. Mavri dis, Irini Moustaki, Martin Knott)
8. Bayesian Structural Equation Modeling (Jesus Palomo, David B. Dunson, Ken Bollen)
9. The Analysis of Structural Equation Model with Ranking Data using Mx (Wai-Yin Poon)
10. Multilevel Structural Equation Modeli ng (Sophia Rable-Hesketh, Anders Skrondal, Xiaohui Zheng)
1
| 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 |
| Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9780444520449
ISBN-10: 0444520449
Series: Handbook of Computing and Statistics With Applications
Published: 1st March 2007
Format: Hardcover
Language: English
Number of Pages: 460
Audience: General Adult
Publisher: NORTH HOLLAND
Country of Publication: US
Dimensions (cm): 24.13 x 17.15 x 2.54
Weight (kg): 0.96
Shipping
| Standard Shipping | Express Shipping | |
|---|---|---|
| Metro postcodes: | $9.99 | $14.95 |
| Regional postcodes: | $9.99 | $14.95 |
| Rural postcodes: | $9.99 | $14.95 |
Orders over $89.00 qualify for free shipping.
How to return your order
At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.
Additional postage charges may be applicable.
Defective items
If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.
For more info please visit our Help Centre.
























