Longitudinal Data Analysis Using Structural Equation Models
American Psychological Association (Verlag)
978-1-4338-1715-1 (ISBN)
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.
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.
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
| Verlagsort | Washington DC |
|---|---|
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Geisteswissenschaften ► Psychologie ► Test in der Psychologie |
| Mathematik / Informatik ► Mathematik | |
| ISBN-10 | 1-4338-1715-2 / 1433817152 |
| ISBN-13 | 978-1-4338-1715-1 / 9781433817151 |
| Zustand | Neuware |
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