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Author:   John J. McArdle ,  John R. Nesselroade
Publisher:   American Psychological Association
Imprint:   American Psychological Association
Dimensions:   Width: 17.80cm , Height: 2.80cm , Length: 25.40cm
Weight:   0.920kg
ISBN:  

9781433817151

ISBN 10:   1433817152
Pages:   426
Publication Date:   16 June 2014
Audience:   College/higher education ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   To order  
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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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"An excellent resource for graduate students and researchers. -- ""Doody's Review Service"""

Author Information

"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 & Marshall College (1973; Lancaster, PA) and both MA and PhD degrees from Hofstra University (1975, 1977; 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, 1992–1993) and the Federation of Behavioral, Cognitive, and Social Sciences (1996–1999). A few other honors include the 1987 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, ""Longitudinal and Adaptive Testing of Adult Cognition"" (2005–2015), 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 (2000–2012), and he also teaches a newer one, Exploratory Data Mining (2009–2014).   John R. Nesselroade, PhD, earned his BS degree in mathematics (Marietta College, Marietta, OH, 1961) and MA and PhD degrees in psychology (University of Illinois at Urbana–Champaign, 1965, 1967).   Prior to moving to the University of Virginia in 1991, Dr. Nesselroade spent 5 years at West Virginia University and 19 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 20 (Adult Development and Aging [1982–1983]) and of SMEP (1999–2000).   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 2010, 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.  "

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