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Structural equation modeling with EQS : basic concepts, applications, and programming / Barbara M. Byrne

Main Author Byrne, Barbara M., 1935- Country Estados Unidos. Edition 2nd ed Publication Mahwah, N.J. : Lawrence Erlbaum Associates, 2006 Description XII, 440 p. : il. ; 24 cm + 1 disco óptico (CD-Rom) ISBN 0-8058-4125-3 CDU 303.7:681.3.06 681.3.06:303.7
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Item type Current location Call number Status Date due Barcode Item holds
Monografia Biblioteca Geral da Universidade do Minho
BGUM 303.7:681.3.06 - B Available 366159
Produtos Computador Biblioteca Geral da Universidade do Minho
BGUM4 303.7:681.3.06 - B Available 366160
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Enhanced descriptions from Syndetics:

Readers who want a less mathematical alternative to the EQS manual will find exactly what they're looking for in this practical text. Written specifically for those with little to no knowledge of structural equation modeling (SEM) or EQS, the author's goal is to provide a non-mathematical introduction to the basic concepts of SEM by applying these principles to EQS, Version 6.1. The book clearly demonstrates a wide variety of SEM/EQS applications that include confirmatory factor analytic and full latent variable models.

Written in a "user-friendly" style, the author "walks" the reader through the varied steps involved in the process of testing SEM models: model specification and estimation, assessment of model fit, EQS output, and interpretation of findings. Each of the book's applications is accompanied by: a statement of the hypothesis being tested, a schematic representation of the model, explanations of the EQS input and output files, tips on how to use the pull-down menus, and the data file upon which the application is based. The book carefully works through applications starting with relatively simple single group analyses, through to more advanced applications, such as a multi-group, latent growth curve, and multilevel modeling.

The new edition features:

many new applications that include a latent growth curve model, a multilevel model, a second-order model based on categorical data, a missing data multigroup model based on the EM algorithm, and the testing for latent mean differences related to a higher-order model; a CD enclosed with the book that includes all application data; vignettes illustrating procedural and/or data management tasks; and description of how to build models both interactively using the BUILD-EQ interface and graphically using the EQS Diagrammer.

Table of contents provided by Syndetics

  • Preface and Acknowledgments (p. ix)
  • I Introduction
  • 1 Structural Equation Models: The Basics (p. 3)
  • Basic Concepts (p. 4)
  • The General Structural Equation Model (p. 9)
  • The General EQS Structural Equation Model (p. 14)
  • 2 Using the EQS Program (p. 18)
  • Components of the EQS Input File (p. 19)
  • The Concept of Model Identification (p. 30)
  • Creating the EQS Input File (p. 37)
  • Building an Input File Manually (p. 36)
  • Building an Input File Interactively Using BUILD_EQS (p. 38)
  • Building an Input File Graphically Using the DIAGRAMMER (p. 48)
  • The EQS Output File in General (p. 71)
  • EQS Error Messages (p. 73)
  • Overview of Remaining Chapters (p. 74)
  • II Single-Group Analyses
  • 3 Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model) (p. 77)
  • The Hypothesized Model (p. 78)
  • The EQS Input File (p. 82)
  • The EQS Output File (p. 86)
  • Model Specification and Analysis Summary (p. 87)
  • Model Assessment (p. 89)
  • Model Misspecification (p. 108)
  • Post Hoc Analyses (p. 112)
  • 4 Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model) (p. 118)
  • The Hypothesized Model (p. 119)
  • The EQS Input File (p. 127)
  • The EQS Output File (p. 129)
  • Post Hoc Analyses (p. 137)
  • 5 Application 3: Testing for the Factorial Validity of Scores From a Measuring Instrument (Second-Order CFA Model) (p. 158)
  • The Hypothesized Model (p. 159)
  • Analysis of Categorical Data (p. 163)
  • Categorical Variables Analyzed as Continuous Variables (p. 163)
  • Categorical Variables Analyzed as Categorical Variables (p. 164)
  • Analyses Based on Data Regarded as Categorical (p. 167)
  • The EQS Input File (p. 167)
  • The EQS Output File (p. 170)
  • Post Hoc Analyses (p. 176)
  • Analyses Based on Data Regarded as Continuous (p. 179)
  • The EQS Output File (p. 179)
  • 6 Application 4: Testing for the Validity of a Causal Structure (p. 186)
  • The Hypothesized Model (p. 186)
  • Formulation of Indicator Variables (p. 188)
  • Confirmatory Factor Analyses (p. 189)
  • The EQS Input File (p. 191)
  • The EQS Output File (p. 199)
  • Post Hoc Analyses (p. 205)
  • III Multiple-Group Analyses
  • 7 Application 5: Testing for the Factorial Invariance of a Measuring Instrument (p. 225)
  • Testing for Multigroup Invariance (p. 226)
  • Testing for Invariance Across Independent Samples (p. 228)
  • The Hypothesized Model (p. 228)
  • The EQS Input File (p. 234)
  • The EQS Output File (p. 237)
  • Other Considerations in Testing for Multiple Group Invariance (p. 245)
  • 8 Application 6: Testing for the Invariance of a Causal Structure (p. 250)
  • Cross-Validation in SEM (p. 250)
  • Testing for Invariance Across Calibration/Validation Samples (p. 252)
  • The Hypothesized Model (p. 253)
  • The EQS Input File (p. 253)
  • The EQS Output File (p. 259)
  • 9 Application 7: Testing for Latent Mean Differences (First-Order CFA Model) (p. 261)
  • Basic Concepts Underlying Tests of Latent Mean Structures (p. 262)
  • Modeling Mean Structures in EQS (p. 263)
  • Testing for Latent Mean Differences of a First-Order CFA Model (p. 267)
  • The Strategy (p. 267)
  • The Hypothesized Model (p. 267)
  • The EQS Input File (p. 274)
  • The EQS Output File (p. 277)
  • 10 Application 8: Testing for Latent Mean Differences (Second-Order CFA Model) (p. 293)
  • Testing for Latent Mean Differences of a Second-Order Model (p. 294)
  • The Strategy (p. 294)
  • The Hypothesized Model (p. 294)
  • IV Other Important Topics
  • 11 Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model (p. 325)
  • The General CFA Approach to MTMM Analyses (p. 330)
  • The Hypothesized Model (p. 330)
  • The EQS Input File (p. 332)
  • The EQS Output File (p. 332)
  • The Correlated Uniqueness Approach to MTMM Analyses (p. 344)
  • The Hypothesized Model (p. 346)
  • The EQS Input File (p. 348)
  • The EQS Output File (p. 348)
  • 12 Application 10: Testing for Change Over Time: The Latent Growth Curve Model (p. 352)
  • Measuring Change in Individual Growth Over Time: The General Notion (p. 354)
  • The Hypothesized Model (p. 354)
  • Modeling Intraindividual Change (p. 354)
  • Modeling Inter-individual Differences in Change (p. 358)
  • Testing for Inter-individual Differences in Change (p. 359)
  • The EQS Input File (p. 362)
  • The EQS Output File (p. 362)
  • Gender as a Time-Invariant Predictor of Change (p. 370)
  • The EQS Input File (p. 373)
  • The EQS Output File (p. 373)
  • 13 Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model (p. 376)
  • Overview of Multilevel Modeling (p. 377)
  • Single-Level Analyses of Hierarchically Structured Data: Related Problems (p. 377)
  • Multiple Level Analyses of Hierarchically Structured Data: Multilevel Modeling (p. 378)
  • The Hypothesized Model (p. 379)
  • The EQS Input File (p. 391)
  • The EQS Output File (p. 396)
  • References (p. 411)
  • Author Index (p. 425)
  • Subject Index (p. 429)

Author notes provided by Syndetics

Barbara M. Byrne is Professor Emeritus in the School of Psychology at the University of Ottawa, Canada

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