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Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis, Fifth Edition 5th edition [Minkštas viršelis]

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(University of Texas at Austin, USA), (Baylor University, USA)
  • Formatas: Paperback / softback, 390 pages, aukštis x plotis: 234x156 mm, weight: 566 g, 100 Line drawings, black and white
  • Išleidimo metai: 14-Feb-2017
  • Leidėjas: Routledge
  • ISBN-10: 1138916072
  • ISBN-13: 9781138916074
Kitos knygos pagal šią temą:
  • Formatas: Paperback / softback, 390 pages, aukštis x plotis: 234x156 mm, weight: 566 g, 100 Line drawings, black and white
  • Išleidimo metai: 14-Feb-2017
  • Leidėjas: Routledge
  • ISBN-10: 1138916072
  • ISBN-13: 9781138916074
Kitos knygos pagal šią temą:
Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing path diagrams to explain the relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models.

The informal writing style and the numerous illustrative examples make the book accessible to readers of varying backgrounds. Notes at the end of each chapter expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapters examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the books examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R.

An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.
Preface ix
Chapter 1 Path Models in Factor, Path, and Structural Equation Analysis
1(36)
Path Diagrams
2(5)
Path Analysis
7(9)
Factor Models
16(7)
Structural Equations
23(1)
Original and Standardized Variables
24(4)
Manifest Versus Latent Variable Models
28(1)
Extended Example
28(3)
Notes
31(2)
Exercises
33(4)
Chapter 2 Fitting Path Models
37(58)
Iterative Solution of Path Equations
37(5)
Matrix Formulation of Path Models
42(4)
Full-Fledged Model-Fitting Programs
46(8)
Fit Functions
54(9)
Hierarchical Χ2 Tests
63(6)
Descriptive Criteria of Model Fit
69(3)
The Power to Reject an Incorrect Model
72(4)
Identification
76(2)
Missing Data
78(4)
Correlations Versus Covariances in Model Fitting
82(2)
Extended Example
84(2)
Notes
86(5)
Exercises
91(4)
Chapter 3 Fitting Path and Structural Models to Data from a Single Group on a Single Occasion
95(38)
Structural and Measurement Models
95(5)
Confirmatory Factor Analysis
100(3)
Some Psychometric Applications of Path and Structural Models
103(8)
Structural Models---Controlling Extraneous Variables
111(4)
Models with Reciprocal Influences and Correlated Errors
115(5)
Nonlinear Effects Among Latent Variables
120(4)
Extended Example
124(2)
Notes
126(3)
Exercises
129(4)
Chapter 4 Fitting Models Involving Repeated Measures, Multiple Groups, or Means
133(38)
Models of Events Over Time
133(9)
Models Comparing Different Groups
142(9)
Fitting Models to Means as well as Covariances
151(8)
The Versatility of Multiple-Group Designs
159(1)
Models with Categorical Indicators
160(3)
A Concluding Comment
163(1)
Extended Example
163(3)
Notes
166(3)
Exercises
169(2)
Chapter 5 Exploratory Factor Analysis---Basics
171(40)
Factor Extraction
174(5)
Estimating Communalities
179(4)
Determining the Number of Factors
183(6)
Rotation
189(8)
An Example: Thurstone's Box Problem
197(4)
Factor Analysis Using Packaged Programs
201(3)
Extended Example
204(1)
Notes
205(3)
Exercises
208(3)
Chapter 6 Exploratory Factor Analysis---Elaborations
211(32)
Rescalings---Alpha and Canonical Factors
211(3)
Alternative Stopping Criteria
214(3)
Alternative Rotation Methods
217(3)
Estimating Factor Scores
220(5)
Hierarchical Factors
225(7)
Nonlinear Factor Analysis
232(3)
Extended Example
235(5)
Notes
240(1)
Exercises
241(2)
Chapter 7 Issues in the Application of Latent Variable Models
243(30)
Exploratory Modification of a Model
243(5)
Alternative Models
248(4)
Can Path Diagrams be Constructed Automatically?
252(3)
Modes of Latent Variable Analysis
255(5)
Advanced Topics in Latent Variable Models
260(3)
Criticisms of Latent Variable Modeling
263(5)
Notes
268(3)
Exercises
271(2)
Appendices
273(36)
A Simple Matrix Operations
273(8)
B Derivation of Matrix Version of Path Equations
281(3)
C LISREL Matrices and Examples
284(5)
D Model Fit Indices
289(10)
E Table of Chi-square Values
299(2)
F Noncentral Chi-square Values for Estimating Power
301(2)
G Power of a Test of Poor Fit and Sample Sizes Needed for Power
303(2)
H Communicating About Latent Variable Models
305(4)
Answers to Exercises 309(12)
References 321(46)
Index 367
John C. Loehlin is Professor Emeritus of Psychology and Computer Science at the

University of Texas at Austin. He received his PhD in Psychology from the University

of California (Berkeley).

A. Alexander Beaujean is an Associate Professor of Educational Psychology at

Baylor University. He received PhDs in Educational Psychology and School

Psychology from the University of Missouri.