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Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)
Download Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)
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Review
"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, Bergstrom Chair in Applied Developmental Science, and Director, Institute for Applied Research in Youth Development, Tufts University  "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, Department of Psychology and Human Development, Vanderbilt University "Little has used his gifts as a researcher, teacher, and writer to create a wonderfully accessible volume that will benefit applied researchers and graduate students alike. Each chapter is complete with highly readable explanations, fresh and interesting examples drawn from the author's own considerable experience, beautifully detailed figures, practical modeling tips and tricks, and extensive supporting materials on the Web, all woven together with welcome doses of humor and personality."--Gregory R. Hancock, PhD, Department of Human Development and Quantitative Methodology, University of Maryland "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, Department of Sociology, The Pennsylvania State University
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About the Author
Todd D. Little, PhD, is Professor of Educational Psychology and Leadership at Texas Tech University and founding Director of the Texas Tech University Institute for Measurement, Methodology, Analysis, and Policy. Dr. Little is a Fellow of the American Association for the Advancement of Science; the American Psychological Association (APA) Divisions 5, 7, and 15; and the Association for Psychological Science. He is past president of APA Division 5 (Evaluation, Measurement, and Statistics). Dr. Little organizes and teaches in his renowned "Stats Camp" each June. Partly because of the impact and importance of Stats Camp, Dr. Little was awarded the Cohen Award from APA Division 5 for Distinguished Contributions to Teaching and Mentoring.
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Product details
Series: Methodology in the Social Sciences
Hardcover: 386 pages
Publisher: The Guilford Press; 1 edition (March 25, 2013)
Language: English
ISBN-10: 1462510167
ISBN-13: 978-1462510160
Product Dimensions:
7.2 x 1 x 10.1 inches
Shipping Weight: 1.9 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
11 customer reviews
Amazon Best Sellers Rank:
#201,559 in Books (See Top 100 in Books)
Not a bad book. The reviews so far seem to be unusually fluffy, definitely influenced me to buy the book to see what I can learn from it - substantively or how to improve my teaching (I also teach a course on SEM). Sure, there are many worse written statistics books, but it is not that great. A solid 3 stars, entirely average.
This is the best and most useful longitudinal latent variable modeling text that I have read. This book has been essential to me in the publication of three papers at this point. In addition, I was able to use sections of the text, especially the chapter on model fit, to address reviewers' suggestions. I also used this text as the primary text in a graduate course on longitudinal SEM. Students found the book extremely helpful with their dissertation research. Finally, Professor Little's writing style is wonderful--he has the unique talent to make complex statistical issues understandable as well as fun to read about!
Easy read and valuable
As far as books on statistical concepts go, this is one of the best. This topic could be taught drly but Dr. Little injects his teaching with real world examples and humor that make it an interesting read. I tried other books to teach me SEM before I found this one and this one is the one that worked for me.
Excellent
Great book! Really helped me with my dissertation analyses, doing crosslag longitudinal panel analyses as well as longitudinal mediation analyses.
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.
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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