Longitudinal Data Analysis Using Structural Equation Models

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.

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

• PrefaceOverviewPart I: FoundationsChapter : Background and Goals of Longitudinal ResearchChapter 2: Basics of Structural Equation ModelingChapter 3: Some Technical Details on Structural Equation ModelingChapter 4: Using the Simplified Reticular Action Model NotationChapter 5: Benefits and Problems Using Structural Equation Modeling in Longitudinal ResearchPart II: Longitudinal SEM for the Direct Identification of Intraindividual ChangesChapter : Alternative Definitions of Individual ChangesChapter 7: Analyses Based on Latent Curve ModelsChapter 8: Analyses Based on Time-Series Regression ModelsChapter 9: Analyses Based on Latent Change Score ModelsChapter : Analyses Based on Advanced Latent Change Score ModelsPart III: Longitudinal SEM for Interindividual Differences in Intraindividual ChangesChapter : Studying Interindividual Differences in Intraindividual ChangesChapter 2: Repeated Measures Analysis of Variance as a Structural ModelChapter 3: Multilevel Structural Equation Modeling Approaches to Group DifferencesChapter 4: Multiple Group Structural Equation Modeling Approaches to Group DifferencesChapter 5: Incomplete Data With Multiple Group Modeling of ChangesPart IV: Longitudinal SEM for the Interrelationships in GrowthChapter : Considering Common Factors/Latent Variables in Structural ModelsChapter 7: Considering Factorial Invariance in Longitudinal Structural Equation ModelingChapter 8: Alternative Common Factors With Multiple Longitudinal ObservationsChapter 9: More Alternative Factorial Solutions for Longitudinal DataChapter 2 : Extensions to Longitudinal Categorical FactorsPart V: Longitudinal SEM for Causes (Determinants) of Intraindividual ChangesChapter 2 : Analyses Based on Cross-Lagged Regression and ChangesChapter 22: Analyses Based on Cross-Lagged Regression in Changes of FactorsChapter 23: Current Models for Multiple Longitudinal Outcome ScoresChapter 24: The Bivariate Latent Change Score Model for Multiple OccasionsChapter 25: Plotting Bivariate Latent Change Score ResultsPart VI: Longitudinal SEM for Interindividual Differences in Causes (Determinants) of Intraindividual ChangesChapter 2 : Dynamic Processes Over GroupsChapter 27: Dynamic Influences Over GroupsChapter 28: Applying a Bivariate Change Model With Multiple GroupsChapter 29: Notes on the Inclusion of Randomization in Longitudinal StudiesChapter 3 : The Popular Repeated Measures Analysis of VariancePart VII: Summary and DiscussionChapter 3 : Contemporary Data Analyses Based on Planned IncompletenessChapter 32: Factor Invariance in Longitudinal ResearchChapter 33: Variance Components for Longitudinal Factor ModelsChapter 34: Models for Intensively Repeated MeasuresChapter 35: Coda: The Future Is Yours!ReferencesIndexAbout the Authors