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Longitudinal Structural Equation Modeling (Methodology in the Social Sciences) [Hardcover]

Todd D. Little

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Book Description

9 May 2013 1462510167 978-1462510160 1

Featuring actual data sets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation. User-friendly features include equation boxes that clearly explain the elements in every equation, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for all of the examples—which include studies of bullying, adolescent students' emotions, and healthy aging—with syntax and output from LISREL, Mplus, and R (lavaan).

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Longitudinal Structural Equation Modeling (Methodology in the Social Sciences) + Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Methodology in the Social Sciences) + Doing Statistical Mediation and Moderation (Methodology in the Social Sciences)
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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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Customer Reviews

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Amazon.com: 5.0 out of 5 stars  4 reviews
3 of 4 people found the following review helpful
5.0 out of 5 stars Amazing book that's more like a mentor than a written lecture 7 Dec 2013
By Wayne Folta - Published on Amazon.com
Format:Hardcover|Verified Purchase
This is an amazing book! It's the kind of book you read and then wonder, "Why aren't all statistical books written like this?". What makes it so incredible is the way that the author describes his thought process, experience, and approach to issues. It feels like you're being mentored rather than lectured.

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.
0 of 2 people found the following review helpful
5.0 out of 5 stars Written for practitioners 29 Jan 2014
By Rascher - Published on Amazon.com
Format:Hardcover|Verified Purchase
Todd has a nice writing style that draws you in (NB. I'm not affiliated with him or his institution). I found this book to be a nice review of SEM and research design--it has useful quotable tips for researchers.
0 of 2 people found the following review helpful
5.0 out of 5 stars Excellent book for longitudinal design and analysis 16 Jan 2014
By joe - Published on Amazon.com
Having taken a number of statistical courses in my PhD program on longitudinal methods, there were still gaps in knowledge that prevented me from working effectively with longitudinal data. This book truly filled those gaps and helped me familiarize myself with aspects of longitudinal modeling that I never knew to consider. Highly recommend, especially for PhD students, postdocs, and faculty in the social sciences.
1 of 4 people found the following review helpful
5.0 out of 5 stars Little (2013) by all means is not little - a “big” book by Little 16 Sep 2013
By Dr. Gabriel Liberman - Published on Amazon.com
Format:Hardcover|Verified Purchase
Prof. Little is among the leading statisticians, especially, in the art of structural equation modeling. In his new book: "Longitudinal Structural Equation Modeling" he adds several aspects. First, he shares his own experience with the readers such that the text becomes very practical and many operational advices are given which save the muddling through time. He is very innovative in introducing several measurement instruments usually not common among lay users of statistics. This introduction stimulates experimentation with alternative models and more complex structures. Overall these advantages, Prof. Little use friendly language and a generous sense of humor, some is ready for quoting, though reading the book require a great deal of efforts to clearly understand the meaning of all terms, figures, and text. In my work as a statistical consultant, I find the book extremely helpful and I cite it repeatedly. Dr. Gabriel Liberman – Data-Graph Statistical Consulting at: [...].
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