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Longitudinal Structural Equation Modeling

ISBN-10: 1462510167
ISBN-13: 9781462510160
Edition: 2013
List price: $70.00 Buy it from $43.09
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Book details

List price: $70.00
Copyright year: 2013
Publisher: Guilford Publications
Publication date: 3/25/2013
Binding: Hardcover
Pages: 386
Size: 7.25" wide x 10.50" long x 1.00" tall
Weight: 1.518
Language: English

Brett Laursen, PhD, is Professor of Psychology and Director of Graduate Training at Florida Atlantic University. He is also a Docent Professor of Social Developmental Psychology at the University of Jyv,,skyl,,, Finland. In 2008, Dr. Laursen received an honorary doctorate from (tm)rebro University, Sweden. He is a Fellow of the American Psychological Association (Division 7, Developmental) and a Fellow and Charter Member of the Association for Psychological Science. In addition to his own research on parent-child and peer relationships, Dr. Laursen is a consultant and collaborator on several large longitudinal projects currently under way in North America and Europe. Todd D. Little, PhD, is Professor of Educational Psychology and Leadership at Texas Tech University and founding Director of the Texas Tech University Research Institute. Dr. Little is past president of the American Psychological Association's Division 5 (Evaluation, Measurement, and Statistics) and winner of the Division's 2013 Cohen Award for Distinguished Contributions to Teaching and Mentoring. He organizes and teaches in the internationally renowned "Stats Camps" that he founded in 2002. Noel A. Card, PhD, is Associate Professor in Family Studies and Human Development at the University of Arizona. His research centers on social development and quantitative methods, and especially the interface of these disciplines. Dr. Card's developmental research focuses on aggression and peer relations during childhood and adolescence; his quantitative interests include longitudinal analyses, analysis of interdependent data, and meta-analysis. He is a recipient of the Society for Research in Child Development's Early Career Research Award.

Overview and Foundations of Structural Equation Modeling
An Overview of the Conceptual Foundations of SEM
Concepts, Constructs, and Indicators
From Concepts to Constructs to Indicators to Good Models
Sources of Variance in Measurement
Classical Test Theorem
Expanding Classical Test Theorem
Characteristics of Indicators and Constructs
Types of Indicators and Constructs
Categorical versus Metrical Indicators and Constructs
Types of Correlation Coefficients That Can Be Modeled
A Simple Taxonomy of Indicators and Their Roles
Reseating Variables
Parceling
What Changes and How?
Some Advice for SEM Programming
Philosophical Issues and How I Approach Research
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Design Issues in Longitudinal Studies
Timing of Measurements and Conceptualizing Time
Cross-Sectional Design
Single-Cohort Longitudinal Design
Cross-Sequential Design
Cohort-Sequential Design
Time-Sequential Design
Other Validity Concerns
Temporal Design
Lags within the Internal of Measurement
Episodic and Experiential Time
Missing Data Imputation and Planned Missing Designs
Missing Data Mechanisms
Recommendations and Caveats
Planned Missing Data Designs in Longitudinal Research
Modeling Developmental Processes in Context
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
The Measurement Model
Drawing and Labeling Conventions
Defining the Parameters of a Construct
Scale Setting
Identification
Adding Means to the Model: Scale Setting and Identification with Means
Adding a Longitudinal Component to the CFA Model
Adding Phantom Constructs to the CFA Model
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Model Fit, Sample Size, and Power
Model Fit and Types of Fit Indices
Statistical Rationale
Modeling Rationale
The Longitudinal Null Model
Summary and Cautions
Sample Size
Power
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
The Longitudinal CFA Model
Factorial Invariance
A Small (Nearly Perfect) Data Example
Configural Factorial Invariance
Weak Factorial Invariance
Strong Factorial Invariance
Evaluating Invariance Constraints
Model Modification
Partial Invariance
A Larger Example Followed by Tests of the Latent Construct Relations
Testing the Latent Construct Parameters
An Application of a Longitudinal SEM to a Repeated-Measures Experiment
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Specifying and Interpreting a Longitudinal Panel Model
Basics of a Panel Model
The Basic Simplex Change Process
Building a Panel Model
Covariate/Control Variables
Building the Panel Model of Positive and Negative Affect
Illustrative Examples of Panel Models
A Simplex Model of Cognitive Development
Two Simplex Models of Nonlongitudinal Data
A Panel Model of Bullying and Homophobic Teasing
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Multiple-Group Models
Multiple-Group Longitudinal SEM
Estimate Missing Data and Evaluate the Descriptive Statistics
Perform Any Supplemental Analysis to Rule Out Potential Confounds
Fit an Appropriate Multiple-Croup Longitudinal Null Model
Fit the Configural Invariant Model across Time and Groups
Test for Weak Factorial (Loadings) Invariance
Test for Strong Factorial (Intercepts) Invariance
Test for Mean-Level Differences in the Latent Constructs
Test for the Homogeneity of the Variance-Covariance Matrix among the Latent Constructs
Test the Longitudinal SEM Model in Each Group
A Dynamic P-Technique Multiple-Group Longitudinal Model
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Multilevel Growth Curves and Multilevel SEM
Longitudinal Growth Curve Model
Multivariate Growth Curve Models
Multilevel Longitudinal Model
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Mediation and Moderation
Making the Distinction between Mediators and Moderators
Cross-Sectional Mediation
Half-Longitudinal Mediation
Full Longitudinal Mediation
Moderation
Summary
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
Jambalaya: Complex Construct Representations and Decompositions
Multitrait-Multimethod Models
Pseudo-MTMM Models
Bifactor and Higher Order Factor Models
Contrasting Different Variance Decompositions
Digestif
Key Terms and Concepts Introduced in This Chapter
Recommended Readings
References
Author Index
Subject Index
About the Author

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