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Methodology in the Social Sciences Ser.: Longitudinal Structural Equation Modeling by Todd D. Little (2024, Hardcover)
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He explains the big picture and technical how-tos of using longitudinal confirmatory factoranalysis, longitudinal panel models, and hybrid models for analyzing within-person change. Chapter on longitudinal mixture modeling, with Whitney Moore.
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About this product
Product Identifiers
PublisherGuilford Publications
ISBN-101462553141
ISBN-139781462553143
eBay Product ID (ePID)24061607655
Product Key Features
Number of Pages608 Pages
LanguageEnglish
Publication NameLongitudinal Structural Equation Modeling
Publication Year2024
SubjectProbability & Statistics / General, Nursing / Research & Theory, Statistics
TypeTextbook
Subject AreaMathematics, Social Science, Psychology, Medical
AuthorTodd D. Little
SeriesMethodology in the Social Sciences Ser.
FormatHardcover
Dimensions
Item Height1.3 in
Item Weight43.6 Oz
Item Length10 in
Item Width7 in
Additional Product Features
Edition Number2
Intended AudienceScholarly & Professional
LCCN2023-038191
Dewey Edition23
Reviews"This is a good core textbook for an advanced course in SEM. It can even be used as a text for an introductory SEM course--as I, myself, have done with the first edition--with a bit of supplementary material. What is special about this book is the extensive use of examples, the end-of-chapter summaries (including definitions), and the detailed discussion of many problems, issues, and controversies--such as whether parceling makes sense, or how to deal with convergence issues or with longitudinal data attrition--not treated extensively in other texts."--Douglas Baer, PhD, Department of Sociology (Emeritus), University of Victoria, British Columbia, Canada "As with the first edition, Little has created not just a wonderful academic resource, but a longitudinal research companion. The second edition features incredibly lucid explanations, useful modeling tips, an extremely accessible style, and cutting-edge updated and new content. Graduate students as well as applied researchers will feel a lot more confident planning for, wading into, and making sense of the intricacies of their longitudinal and developmental phenomena."--Gregory R. Hancock, PhD, Department of Human Development and Quantitative Methodology, University of Maryland, College Park "In its second edition, this remains the definitive text on longitudinal SEM. The biggest strength of all the chapters is that they follow a clear organization and flow. Basic issues are presented first, followed by more advanced issues, and, finally, an example or two of the topic, with real data."--Kristin D. Mickelson, PhD, School of Social and Behavioral Sciences, Arizona State University "Longitudinal SEM is tricky, even for people who have experience with factor analysis and other related models. I recommend the second edition of this book to applied researchers looking for a nontechnical overview. It will help readers build their intuitive understanding of the models, which can provide a foundation for future study."--Ed Merkle, PhD, Department of Psychological Sciences, University of Missouri-Columbia "The equation boxes are a really nice touch that make it easier for readers to decipher the content in the equations. I am used to seeing notation detailed in paragraph-style text under an equation, but I am sold--this is a much clearer presentation style."--Sarah Depaoli, PhD, Department of Psychological Sciences, University of California, Merced, "In its second edition, this remains the definitive text on longitudinal SEM. The biggest strength of all the chapters is that they follow a clear organization and flow. Basic issues are presented first, followed by more advanced issues, and, finally, an example or two of the topic, with real data."--Kristin D. Mickelson, PhD, School of Social and Behavioral Sciences, Arizona State University "Longitudinal SEM is tricky, even for people who have experience with factor analysis and other related models. I recommend the second edition of this book to applied researchers looking for a nontechnical overview. It will help readers build their intuitive understanding of the models, which can provide a foundation for future study."--Ed Merkle, PhD, Department of Psychological Sciences, University of Missouri-Columbia "This is a good core textbook for an advanced course in SEM. It can even be used as a text for an introductory SEM course--as I, myself, have done with the first edition--with a bit of supplementary material. What is special about this book is the extensive use of examples, the end-of-chapter summaries (including definitions), and the detailed discussion of many problems, issues, and controversies--such as whether parceling makes sense, or how to deal with convergence issues or with longitudinal data attrition--not treated extensively in other texts."--Douglas Baer, PhD, Department of Sociology (Emeritus), University of Victoria, British Columbia, Canada "The equation boxes are a really nice touch that make it easier for readers to decipher the content in the equations. I am used to seeing notation detailed in paragraph-style text under an equation, but I am sold--this is a much clearer presentation style."--Sarah Depaoli, PhD, Department of Psychological Sciences, University of California, Merced, "In its second edition, this remains the definitive text on longitudinal SEM. The biggest strength of all the chapters is that they follow a clear organization and flow. Basic issues are presented first, followed by more advanced issues, and, finally, an example or two of the topic, with real data."--Kristin D. Mickelson, PhD, School of Social and Behavioral Sciences, Arizona State University "Longitudinal SEM is tricky, even for people who have experience with factor analysis and other related models. I recommend the second edition of this book to applied researchers looking for a nontechnical overview. It will help readers build their intuitive understanding of the models, which can provide a foundation for future study."--Ed Merkle, PhD, Department of Psychological Sciences, University of Missouri-Columbia "This is a good core textbook for an advanced course in SEM. It can even be used as a text for an introductory SEM course--as I, myself, have done with the first edition--with a bit of supplementary material. What is special about this book is the extensive use of examples, the end-of-chapter summaries (including definitions), and the detailed discussion of many problems, issues, and controversies--such as whether parceling makes sense, or how to deal with convergence issues or with longitudinal data attrition--not treated extensively in other texts."--Douglas Baer, PhD, Department of Sociology (Emeritus), University of Victoria, British Columbia, Canada "The equation boxes are a really nice touch that make it easier for readers to decipher the content in the equations. I am used to seeing notation detailed in paragraph-style text under an equation, but I am sold--this is a much clearer presentation style."--Sarah Depaoli, PhD, Department of Psychological Sciences, University of California, Merced-
Dewey Decimal300.727
SynopsisThis valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesn't work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents' emotions, and healthy aging--along with syntax and output, chapter quizzes, and the book's figures. New to This Edition: *Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux. *Chapter on longitudinal mixture modeling, with Whitney Moore. *Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne. *Chapter on Bayesian SEM, with Mauricio Garnier. *Revised throughout with new developments and discussions, such as how to test models of experimental effects., Beloved for its engaging, conversational style, this valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesnt work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents emotions, and healthy aging--along with syntax and output, chapter quizzes, and the books figures. New to This Edition: *Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux. *Chapter on longitudinal mixture modeling, with Whitney Moore. *Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne. *Chapter on Bayesian SEM, with Mauricio Garnier. *Revised throughout with new developments and discussions, such as how to test models of experimental effects., Beloved for its engaging, conversational style, this valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM.
LC Classification NumberHA29.L83175 2023
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