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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 : 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 : 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 : Analyses Based on Advanced Latent Change Score Models Part III: Longitudinal SEM for Interindividual Differences in Intraindividual Changes Chapter : Studying Interindividual Differences in Intraindividual Changes Chapter 2: Repeated Measures Analysis of Variance as a Structural Model Chapter 3: Multilevel Structural Equation Modeling Approaches to Group Differences Chapter 4: Multiple Group Structural Equation Modeling Approaches to Group Differences Chapter 5: Incomplete Data With Multiple Group Modeling of Changes Part IV: Longitudinal SEM for the Interrelationships in Growth Chapter : Considering Common Factors/Latent Variables in Structural Models Chapter 7: Considering Factorial Invariance in Longitudinal Structural Equation Modeling Chapter 8: Alternative Common Factors With Multiple Longitudinal Observations Chapter 9: More Alternative Factorial Solutions for Longitudinal Data Chapter 2 : Extensions to Longitudinal Categorical Factors Part V: Longitudinal SEM for Causes (Determinants) of Intraindividual Changes Chapter 2 : 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 2 : 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 3 : The Popular Repeated Measures Analysis of Variance Part VII: Summary and Discussion Chapter 3 : 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

About the Author :
John J. (Jack) McArdle, PhD, is senior professor of psychology at the University of Southern California (USC), where he heads the Quantitative Methods Area and has been chair of the USC Research Committee.   He received a BA from Franklin amp amp Marshall College ( 973 Lancaster, PA) and both MA and PhD degrees from Hofstra University ( 975, 977 Hempstead, NY). He now teaches classes in psychometrics, multivariate analysis, longitudinal data analysis, exploratory data mining, and structural equation modeling at USC.   His research was initially focused on traditional repeated measures analyses and moved toward age-sensitive methods for psychological and educational measurement and longitudinal data analysis, including publications in factor analysis, growth curve analysis, and dynamic modeling of abilities.   Dr. McArdle is a fellow of the American Association for the Advancement of Science (AAAS). He served as president of the Society of Multivariate Experimental Psychology (SMEP, 992 amp ndash 993) and the Federation of Behavioral, Cognitive, and Social Sciences ( 99 amp ndash 999). A few other honors include the 987 R. B. Cattell Award for Distinguished Multivariate Research from SMEP.   Dr. McArdle was recently awarded an National Institutes of Health-MERIT grant from the National Institute on Aging for his work, amp quot Longitudinal and Adaptive Testing of Adult Cognition amp quot (2 5 amp ndash 2 5), where he is working on new adaptive tests procedures to measure higher order cognition as a part of large-scale surveys (e.g. the Human Resources Services).   Working with APA, he has created and led the Advanced Training Institute on Longitudinal Structural Equation Modeling (2 amp ndash 2 2), and he also teaches a newer one, Exploratory Data Mining (2 9 amp ndash 2 4).   John R. Nesselroade, PhD, earned his BS degree in mathematics (Marietta College, Marietta, OH, 9 ) and MA and PhD degrees in psychology (University of Illinois at Urbana amp ndash Champaign, 9 5, 9 7).   Prior to moving to the University of Virginia in 99 , Dr. Nesselroade spent 5 years at West Virginia University and 9 years at The Pennsylvania State University. He has been a frequent visiting scientist at the Max Planck Institute for Human Development, Berlin. He is a past-president of APA's Division 2 (Adult Development and Aging [ 982 amp ndash 983]) and of SMEP ( 999 amp ndash 2 ).   Dr. Nesselroade is a fellow of the AAAS, the APA, the Association for Psychological Science, and the Gerontological Society of America. Other honors include the R. B. Cattell Award for Distinguished Multivariate Research and the S. B. Sells Award for Distinguished Lifetime Achievement from SMEP.   Dr. Nesselroade has also won the Gerontological Society of America's Robert F. Kleemeier Award. In 2 , he received an Honorary Doctorate from Berlin's Humboldt University. He is currently working on the further integration of individual level analyses into mainstream behavioral research.   The two authors have worked together in enjoyable collaborations for more than 25 years.  

Review :
An excellent resource for graduate students and researchers. (Doody's Review Service)


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Product Details
  • ISBN-13: 9781433817151
  • Publisher: American Psychological Association
  • Publisher Imprint: American Psychological Association
  • Height: 254 mm
  • No of Pages: 426
  • ISBN-10: 1433817152
  • Publisher Date: 16 Jun 2014
  • Binding: Hardback
  • Language: English
  • Width: 178 mm


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