- Hardcover: 386 pages
- Publisher: Guilford Press (10 May 2013)
- Language: English
- ISBN-10: 1462510167
- ISBN-13: 978-1462510160
- Product Dimensions: 25.7 x 18.3 x 2.5 cm
- Amazon Bestsellers Rank: 539,275 in Books (See Top 100 in Books)
- See Complete Table of Contents
Longitudinal Structural Equation Modeling (Methodology in the Social Sciences) Hardcover – 10 May 2013
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"Novices and experts alike will learn something new from this book. Little is a born teacher, and it shows in his writing. His approach assumes little background knowledge and provides an entrée to the literature for students and researchers who want to know more. Examples from Little's experience as an applied researcher make the concepts concrete and accessible. This is an ideal text to accompany graduate courses on SEM or longitudinal data analysis and a useful reference for researchers who want to add longitudinal SEM to their methodological toolboxes." - Kristopher J. Preacher, PhD, Vanderbilt University, Tennessee, USA
"It is rare for a scholar or a teacher to simultaneously demonstrate wisdom, erudition, vision for the future of the field, and the capacity to explain complex ideas and methods to beginners, while also advancing the skill sets of seasoned researchers. Yet these valued attributes are all found in abundance in this volume. This is more than a book about longitudinal SEM; it is a guide to understanding and conducting good science. If any book can be identified as a classic on publication, this one certainly can." - Richard M. Lerner, PhD, Tufts University, Massachusetts, USA
"Little leads readers through a thoughtful and pragmatic approach to SEM by explaining how to think about longitudinal designs, weigh modeling options, and make informed decisions. Developed in both conceptual and technical terms, and illustrated with social science examples, this book is particularly suited to those who follow words and sentences more easily than they track symbols and mathematical operators." - Melissa Hardy, PhD, The Pennsylvania State University, USA
About the Author
Todd D. Little, PhD, is Professor of Psychology, Director of the Quantitative Training Program, and a member of the Developmental Training Program at the University of Kansas (KU), where he is also Director of the Center for Research Methods and Data Analysis. He is editor of Guilford's Methodology in the Social Sciences series. Past president of the American Psychological Association's Division 5 (Evaluation, Measurement, and Statistics), Dr. Little organizes and teaches in the renowned KU "Stats Camps" each June.
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If Amazon let you add a sixth star for just a couple of books over your purchasing history, this one would get my extra star.
As one example of what I mean, he spends five pages discussing the design of the timing of measurements that you will take, and the effects that this timing can have. Many measurements will be taken over a time window, and the location and duration of that window can have different effects. (Effects that SEM is well able to deal with, if you have a good design and a model which takes it into account.) It's really encouraging to read someone who says, "I've been guilty of this thinking in my own past work. ... In my older datasets, I won't know whether they did ... Had I known then what I know now, I would have ... The more I work with researchers and study development chafes, the more I realize ..." As I said, more like a mentor than a lecturer.
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