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Longitudinal Data Analysis Using Structural Equation Models

Longitudinal Data Analysis Using Structural Equation Models

          
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About the Book

When determining the most appropriate method for analyzing longitudinal data, you must first consider what research question you want to answer. In this book, McArdle and Nesselroade identify five basic purposes of longitudinal structural equation modeling. For each purpose, they present the most useful strategies and models. Two important but underused approaches are emphasized: multiple factorial invariance over time and latent change scores. The book covers a wealth of models in a straightforward, understandable manner. Rather than overwhelm the reader with an extensive amount of algebra, the authors use path diagrams and emphasize methods that are appropriate for many uses.

Table of Contents:
Preface Overview Part I: Foundations Chapter 1: Background and Goals of Longitudinal Research Chapter 2: Basics of Structural Equation Modeling Chapter 3: Some Technical Details on Structural Equation Modeling Chapter 4: Using the Simplified Reticular Action Model Notation Chapter 5: Benefits and Problems Using Structural Equation Modeling in Longitudinal Research Part II: Longitudinal SEM for the Direct Identification of Intraindividual Changes Chapter 6: Alternative Definitions of Individual Changes Chapter 7: Analyses Based on Latent Curve Models Chapter 8: Analyses Based on Time-Series Regression Models Chapter 9: Analyses Based on Latent Change Score Models Chapter 10: Analyses Based on Advanced Latent Change Score Models Part III: Longitudinal SEM for Interindividual Differences in Intraindividual Changes Chapter 11: Studying Interindividual Differences in Intraindividual Changes Chapter 12: Repeated Measures Analysis of Variance as a Structural Model Chapter 13: Multilevel Structural Equation Modeling Approaches to Group Differences Chapter 14: Multiple Group Structural Equation Modeling Approaches to Group Differences Chapter 15: Incomplete Data With Multiple Group Modeling of Changes Part IV: Longitudinal SEM for the Interrelationships in Growth Chapter 16: Considering Common Factors/Latent Variables in Structural Models Chapter 17: Considering Factorial Invariance in Longitudinal Structural Equation Modeling Chapter 18: Alternative Common Factors With Multiple Longitudinal Observations Chapter 19: More Alternative Factorial Solutions for Longitudinal Data Chapter 20: Extensions to Longitudinal Categorical Factors Part V: Longitudinal SEM for Causes (Determinants) of Intraindividual Changes Chapter 21: Analyses Based on Cross-Lagged Regression and Changes Chapter 22: Analyses Based on Cross-Lagged Regression in Changes of Factors Chapter 23: Current Models for Multiple Longitudinal Outcome Scores Chapter 24: The Bivariate Latent Change Score Model for Multiple Occasions Chapter 25: Plotting Bivariate Latent Change Score Results Part VI: Longitudinal SEM for Interindividual Differences in Causes (Determinants) of Intraindividual Changes Chapter 26: Dynamic Processes Over Groups Chapter 27: Dynamic Influences Over Groups Chapter 28: Applying a Bivariate Change Model With Multiple Groups Chapter 29: Notes on the Inclusion of Randomization in Longitudinal Studies Chapter 30: The Popular Repeated Measures Analysis of Variance Part VII: Summary and Discussion Chapter 31: Contemporary Data Analyses Based on Planned Incompleteness Chapter 32: Factor Invariance in Longitudinal Research Chapter 33: Variance Components for Longitudinal Factor Models Chapter 34: Models for Intensively Repeated Measures Chapter 35: Coda: The Future Is Yours! References Index About the Authors


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Product Details
  • ISBN-13: 9781433817151
  • Publisher: American Psychological Association (APA)
  • Publisher Imprint: American Psychological Association (APA)
  • Depth: 32
  • Height: 263 mm
  • No of Pages: 426
  • Spine Width: 28 mm
  • Width: 185 mm
  • ISBN-10: 1433817152
  • Publisher Date: 16 Jun 2014
  • Binding: Hardback
  • Edition: 1
  • Language: English
  • Returnable: Y
  • Weight: 942 gr


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