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E-raamat: Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Third Edition 3rd edition [Taylor & Francis e-raamat]

(University of Ottawa, Canada)
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This bestselling text provides a practical guide to structural equation modeling (SEM) using the Amos Graphical approach. Using clear, everyday language, the text is ideal for those with little to no exposure to either SEM or Amos. The author reviews SEM applications based on actual data taken from her own research. Each chapter "walks" readers through the steps involved (specification, estimation, evaluation, and post hoc modification) in testing a variety of SEM models. Accompanying each application is: an explanation of the issues addressed and a schematic presentation of hypothesized model structure; Amos input and output with interpretations; use of the Amos toolbar icons and pull-down menus; and data upon which the model application was based, together with updated references pertinent to the SEM model tested.

Thoroughly updated throughout, the new edition features:











All new screen shots featuring Amos Version 23.





Descriptions and illustrations of Amos new Tables View format which enables the specification of a structural model in spreadsheet form.





Key concepts and/or techniques that introduce each chapter.





Alternative approaches to model analyses when enabled by Amos thereby allowing users to determine the method best suited to their data.





Provides analysis of the same model based on continuous and categorical data (Ch. 5) thereby enabling readers to observe two ways of specifying and testing the same model as well as compare results.





All applications based on the Amos graphical mode interface accompanied by more "how to" coverage of graphical techniques unique to Amos.





More explanation of key procedures and analyses that address questions posed by readers





All application data files are available at www.routledge.com/9781138797031.

The two introductory chapters in Section 1 review the fundamental concepts of SEM methodology and a general overview of the Amos program. Section 2 provides single-group analyses applications including two first-order confirmatory factor analytic (CFA) models, one second-order CFA model, and one full latent variable model. Section 3 presents multiple-group analyses applications with two rooted in the analysis of covariance structures and one in the analysis of mean and covariance structures. Two models that are increasingly popular with SEM practitioners, construct validity and testing change over time using the latent growth curve, are presented in Section 4. The book concludes with a review of the use of bootstrapping to address non-normal data and a review of missing (or incomplete) data in Section 5.

An ideal supplement for graduate level courses in psychology, education, business, and social and health sciences that cover the fundamentals of SEM with a focus on Amos, this practical text continues to be a favorite of both researchers and practitioners. A prerequisite of basic statistics through regression analysis is recommended but no exposure to either SEM or Amos is required.
Preface xvi
Acknowledgments xxi
About the Author xxii
Section I Introduction
Chapter 1 Structural Equation Modeling: The Basics
3(13)
Key Concepts
3(1)
What Is Structural Equation Modeling?
3(1)
Basic Concepts
4(5)
Latent versus Observed Variables
4(1)
Exogenous versus Endogenous Latent Variables
5(1)
The Factor Analytic Model
5(2)
The Full Latent Variable Model
7(1)
General Purpose and Process of Statistical Modeling
7(2)
The General Structural Equation Model
9(6)
Symbol Notation
9(1)
The Path Diagram
10(1)
Structural Equations
11(1)
Nonvisible Components of a Model
12(1)
Basic Composition
13(1)
The Formulation of Covariance and Mean Structures
14(1)
Notes
15(1)
Chapter 2 Using the Amos Program
16(53)
Key Concepts
16(2)
Model Specification Using Amos Graphics (Example 1)
18(15)
Amos Modeling Tools
19(4)
The Hypothesized Model
23(1)
Drawing the Path Diagram
23(10)
Model Specification Using Amos Tables View (Example 1)
33(10)
Understanding the Basic Components of Model 1
40(1)
The Concept of Model Identification
40(3)
Model Specification Using Amos Graphics (Example 2)
43(8)
The Hypothesized Model
43(3)
Drawing the Path Diagram
46(5)
Model Specification Using Amos Tables View (Example 2)
51(2)
Model Specification Using Amos Graphics (Example 3)
53(5)
The Hypothesized Model
54(1)
Drawing the Path Diagram
55(3)
Changing the Amos Default Color for Constructed Models
58(3)
Model Specification Using Amos Tables View (Example 3)
61(2)
Notes
63(6)
Section II Single-Group Analyses
Confirmatory Factor Analytic Models
Chapter 3 Application 1: Testing the Factorial Validity of a Theoretical Construct (First-Order CFA Model)
69(46)
Key Concepts
69(1)
The Hypothesized Model
70(1)
Hypothesis 1 Self-concept is a 4-Factor Structure
70(5)
Modeling with Amos Graphics
75(32)
Model Specification
75(1)
Data Specification
75(3)
Calculation of Estimates
78(3)
Amos Text Output: Hypothesized 4-Factor Model
81(1)
Model Summary
81(1)
Model Variables and Parameters
82(1)
Model Evaluation
82(2)
Parameter Estimates
84(2)
Model as a Whole
86(16)
Model Misspecification
102(5)
Post Hoc Analyses
107(1)
Hypothesis 2 Self-concept is a 2-Factor Structure
108(2)
Selected Amos Text Output: Hypothesized 2-Factor Model
110(1)
Hypothesis 3 Self-concept is a 1-Factor Structure
110(1)
Modeling with Amos Tables View
111(2)
Notes
113(2)
Chapter 4 Application 2: Testing the Factorial Validity of Scores from a Measurement Scale (First-Order CFA Model)
115(34)
Key Concepts
115(1)
Modeling with Amos Graphics
115(2)
The Measuring Instrument under Study
116(1)
The Hypothesized Model
117(14)
Selected Amos Output: The Hypothesized Model
119(7)
Model Evaluation
126(5)
Post Hoc Analyses
131(1)
Model 2
132(4)
Selected Amos Output: Model 2
132(4)
Model 3
136(3)
Selected Amos Output: Model 3
136(3)
Model 4
139(7)
Selected Amos Output: Model 4
139(6)
Comparison with Robust Analyses Based on the Satorra--Bentler Scaled Statistic
145(1)
Modeling with Amos Tables View
146(2)
Notes
148(1)
Chapter 5 Application 3: Testing the Factorial Validity of Scores from a Measurement Scale (Second-Order CFA Model)
149(36)
Key Concepts
149(1)
The Hypothesized Model
150(2)
Modeling with Amos Graphics
152(11)
Selected Amos Output File: Preliminary Model
155(6)
Selected Amos Output: The Hypothesized Model
161(1)
Model Evaluation
161(2)
Estimation Based on Continous Versus Categorical Data
163(7)
Categorical Variables Analyzed as Continuous Variables
167(1)
Categorical Variables Analyzed as Categorical Variables
168(2)
The Amos Approach to Analysis of Categorical Variables
170(10)
What is Bayesian Estimation?
171(1)
Application of Bayesian Estimation
171(9)
Modeling with Amos Tables View
180(2)
Note
182(3)
Full Latent Variable Model
Chapter 6 Application 4: Testing the Validity of a Causal Structure
185(42)
Key Concepts
185(1)
The Hypothesized Model
186(1)
Modeling with Amos Graphics
187(16)
Formulation of Indicator Variables
187(2)
Confirmatory Factor Analyses
189(8)
Selected Amos Output: Hypothesized Model
197(2)
Model Assessment
199(4)
Post Hoc Analyses
203(16)
Selected Amos Output: Model 2
203(1)
Model Assessment
203(1)
Selected Amos Output: Model 3
204(1)
Model Assessment
204(1)
Selected Amos Output: Model 4
205(1)
Model Assessment
205(1)
Selected Amos Output: Model 5
206(1)
Model Assessment
206(2)
Selected Amos Output: Model 6
208(1)
Model Assessment
208(1)
The Issue of Model Parsimony
208(2)
Selected Amos Output: Model 7 (Final Model)
210(1)
Model Assessment
210(2)
Parameter Estimates
212(7)
Modeling with Amos Tables View
219(2)
Notes
221(6)
Section III Multiple-Group Analyses
Confirmatory Factor Analytic Models
Chapter 7 Application 5: Testing Factorial Invariance of Scales from a Measurement Scale (First-Order CFA Model)
227(36)
Key Concepts
227(2)
Testing For Multigroup Invariance
229(1)
The General Notion
229(1)
The Testing Strategy
230(1)
The Hypothesized Model
230(5)
Establishing Baseline Models: The General Notion
231(1)
Establishing the Baseline Models: Elementary and Secondary Teachers
231(4)
Modeling with Amos Graphics
235(3)
Hierarchy of Steps in Testing Multigroup Invariance
238(23)
I Testing for Configural Invariance
238(2)
Selected Amos Output: The Configural Model (No Equality Constraints Imposed)
240(4)
II Testing for Measurement and Structural Invariance: The Specification Process
244(8)
III Testing for Measurement and Structural Invariance: Model Assessment
252(1)
Testing For Multigroup Invariance: The Measurement Model
253(1)
Model Assessment
253(8)
Testing For Multigroup Invariance: The Structural Model
261(1)
Notes
261(2)
Chapter 8 Application 6: Testing Invariance of Latent Mean Structures (First-Order CFA Model)
263(30)
Key Concepts
263(1)
Basic Concepts Underlying Tests of Latent Mean Structures
264(3)
Estimation of Latent Variable Means
266(1)
The Hypothesized Model
267(2)
The Baseline Models
269(1)
Modeling with Amos Graphics
269(2)
The Structured Means Model
269(2)
Testing for Latent Mean Differences
271(18)
The Hypothesized Multigroup Model
271(1)
Steps in the Testing Process
271(8)
Selected Amos Output: Model Summary
279(2)
Selected Amos Output: Goodness-of-fit Statistics
281(2)
Selected Amos Output: Parameter Estimates
283(6)
Notes
289(4)
Full Latent Variable Model
Chapter 9 Application 7: Testing Invariance of a Causal Structure (Full Structural Equation Model)
293(18)
Key Concepts
293(1)
Cross-Validation in Covariance Structure Modeling
293(3)
Testing for Invariance across Calibration/Validation Samples
296(1)
The Hypothesized Model
296(7)
Establishing a Baseline Model
298(5)
Modeling with Amos Graphics
303(8)
Testing for the Invariance of Causal Structure Using the Automated Multigroup Approach
303(2)
Selected Amos Output: Goodness-of-fit Statistics for Comparative Tests of Multigroup Invariance
305(6)
Section IV Other Important Applications
Chapter 10 Application 8: Testing Evidence of Construct Validity: The Multitrait-Multimethod Model
311(28)
Key Concepts
311(2)
The Correlated Traits-Correlated Methods Approach to MTMM Analyses
313(14)
Model 1 Correlated Traits-Correlated Methods
315(5)
Model 2 No Traits-Correlated Methods
320(2)
Model 3 Perfectly Correlated Traits-Freely Correlated Methods
322(4)
Model 4 Freely Correlated Traits-Uncorrelated Methods
326(1)
Testing for Evidence of Convergent and Discriminant Validity: MTMM Matrix-level Analyses
327(1)
Comparison of Models
327(1)
Evidence of Convergent Validity
327(1)
Evidence of Discriminant Validity
327(1)
Testing for Evidence of Convergent and Discriminant Validity: MTMM Parameter-level Analyses
328(3)
Examination of Parameters
328(1)
Evidence of Convergent Validity
329(2)
Evidence of Discriminant Validity
331(1)
The Correlated Uniquenesses Approach to MTMM Analyses
331(7)
Model 5 Correlated Uniqueness Model
335(3)
Notes
338(1)
Chapter 11 Application 9: Testing Change Over Time: The Latent Growth Curve Model
339(26)
Key Concepts
339(2)
Measuring Change in Individual Growth over Time: The General Notion
341(1)
The Hypothesized Dual-domain LGC Model
341(5)
Modeling Intraindividual Change
341(4)
Modeling Interindividual Differences in Change
345(1)
Testing Latent Growth Curve Models: A Dual-Domain Model
346(8)
The Hypothesized Model
346(4)
Selected Amos Output: Hypothesized Model
350(4)
Testing Latent Growth Curve Models: Gender as a Time-invariant Predictor of Change
354(7)
Notes
361(4)
Section V Other Important Topics
Chapter 12 Application 10: Use of Bootstrapping in Addressing Nonnormal Data
365(28)
Key Concepts
365(3)
Basic Principles Underlying the Bootstrap Procedure
368(2)
Benefits and Limitations of the Bootstrap Procedure
369(1)
Caveats Regarding the Use of Bootstrapping in SEM
369(1)
Modeling with Amos Graphics
370(5)
The Hypothesized Model
371(1)
Characteristics of the Sample
371(2)
Applying the Bootstrap Procedure
373(2)
Selected Amos Output
375(17)
Parameter Summary
375(1)
Assessment of Normality
376(2)
Parameter Estimates and Standard Errors
378(14)
Note
392(1)
Chapter 13 Application 11: Addressing the Issues of Missing Data
393(14)
Key Concepts
393(1)
Basic Patterns of Missing Data
394(2)
Common Approaches to Handling Incomplete Data
396(4)
Ad Hoc Approaches to Handling Missing Data (Not recommended)
396(3)
Theory-based Approaches to Handling Missing Data (Recommended)
399(1)
The Amos Approach to Handling Missing Data
400(1)
Modeling with Amos Graphics
401(5)
The Hypothesized Model
401(3)
Selected Amos Output: Parameter and Model Summary Information
404(1)
Selected Amos Output: Parameter Estimates
405(1)
Selected Amos Output: Goodness-of-fit Statistics
406(1)
Note
406(1)
References 407(21)
Author Index 428(5)
Subject Index 433
Barbara M. Byrne is Professor Emeritus in the School of Psychology, University of Ottawa, Canada.