Longitudinal Data Analysis Using Structural Equation Models - Hardcover

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 an MA and the PhD 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. 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).