Free Download Latent Curve Models By Kenneth Bollen
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An efficient method for social science data analysis
The need for this current book is underscored by the recent increase in longitudinal data in the social sciences. A useful method for analyzing latent curve models (LCMs) is Latent Curve Models: A Structural Equation Perspective. Each case in a sample can have a unique trajectory over time thanks to the random intercepts and slopes seen in this type of data. In order to forecast the parameters controlling these trajectories, researchers can also incorporate variables.
In addition to providing unique results, the writers synthesize a great deal of research and findings. The book examines LCMs from the standpoint of latent variable structural equation models (SEMs). The majority of the presentation employs SEMs as a driving tool, even if the authors also cover basic regression-based techniques that are helpful in the early phases of LCMs. This innovative paper examines repeated latent variable models, proposes novel models for method factors in multiple indicators, identifies a variety of LCMs, and incorporates some of the authors’ previous work on the autoregressive latent trajectory model.
To help readers grasp and apply LCMs to their own data sets quickly and simply, this text has undergone intensive class testing and makes heavy use of pedagogical techniques. Important characteristics include:
- Introductions to chapters and synopses that give a brief rundown of the main points
- Throughout, readers are given empirical examples to test their newly acquired information and find real-world applications.
- Chapter-ending conclusions that highlight the key ideas readers must grasp in order to move on to more complex subjects
- Extensive footnoting that directs readers to the original research on specific subjects
This is a great book for graduate courses on latent trajectory models and can be used as a supplemental text for structural modeling classes because of its focus on modeling and usage of many examples. For scholars in the quantitative social and behavioral sciences who must examine longitudinal data, this book is a great tool and resource.
Reviews of Editorials
Examine
“This helpful new book on growth curve modeling closes a significant gap in the literature on applied methodology in longitudinal modeling. In our opinion, it is a crucial text that anyone involved in longitudinal modeling should possess and be able to use while developing and teaching models. (2011, Psychometrika)
“…an authoritative account of the subject…” (American Statistical Association Journal, December 2007)
From the Flap Inside
An efficient method for social science data analysis
The need for this current book is underscored by the recent increase in longitudinal data in the social sciences. A useful method for analyzing latent curve models (LCMs) is Latent Curve Models: A Structural Equation Perspective. Each case in a sample can have a unique trajectory over time thanks to the random intercepts and slopes seen in this type of data. In order to forecast the parameters controlling these trajectories, researchers can also incorporate variables.
In addition to providing unique results, the writers synthesize a great deal of research and findings. The book examines LCMs from the standpoint of latent variable structural equation models (SEMs). The majority of the presentation employs SEMs as a driving tool, even if the authors also cover basic regression-based techniques that are helpful in the early phases of LCMs. This innovative paper examines repeated latent variable models, proposes novel models for method factors in multiple indicators, identifies a variety of LCMs, and incorporates some of the authors’ previous work on the autoregressive latent trajectory model.
To help readers grasp and apply LCMs to their own data sets quickly and simply, this text has undergone intensive class testing and makes heavy use of pedagogical techniques. Important characteristics include:
Introductions to chapters and synopses that give a brief rundown of the main points
Throughout, readers are given empirical examples to test their newly acquired information and find real-world applications.
Chapter-ending conclusions that highlight the key ideas readers must grasp in order to move on to more complex subjects
Extensive footnoting that directs readers to the original research on specific subjects
This is a great book for graduate courses on latent trajectory models and can be used as a supplemental text for structural modeling classes because of its focus on modeling and usage of many examples. For scholars in the quantitative social and behavioral sciences who must examine longitudinal data, this book is a great tool and resource.
From the Rear Cover
An efficient method for social science data analysis
The need for this current book is underscored by the recent increase in longitudinal data in the social sciences. A useful method for analyzing latent curve models (LCMs) is Latent Curve Models: A Structural Equation Perspective. Each case in a sample can have a unique trajectory over time thanks to the random intercepts and slopes seen in this type of data. In order to forecast the parameters controlling these trajectories, researchers can also incorporate variables.
In addition to providing unique results, the writers synthesize a great deal of research and findings. The book examines LCMs from the standpoint of latent variable structural equation models (SEMs). The majority of the presentation employs SEMs as a driving tool, even if the authors also cover basic regression-based techniques that are helpful in the early phases of LCMs. This innovative paper examines repeated latent variable models, proposes novel models for method factors in multiple indicators, identifies a variety of LCMs, and incorporates some of the authors’ previous work on the autoregressive latent trajectory model.
To help readers grasp and apply LCMs to their own data sets quickly and simply, this text has undergone intensive class testing and makes heavy use of pedagogical techniques. Important characteristics include:
- Introductions to chapters and synopses that give a brief rundown of the main points
- Throughout, readers are given empirical examples to test their newly acquired information and find real-world applications.
- Chapter-ending conclusions that highlight the key ideas readers must grasp in order to move on to more complex subjects
- Extensive footnoting that directs readers to the original research on specific subjects
This is a great book for graduate courses on latent trajectory models and can be used as a supplemental text for structural modeling classes because of its focus on modeling and usage of many examples. For scholars in the quantitative social and behavioral sciences who must examine longitudinal data, this book is a great tool and resource.
Concerning the Writer
KENNETH A. BOLLEN, PhD, is an adjunct professor of statistics at The University of North Carolina at Chapel Hill, the director of the Odum Institute for Research in Social Science, and the Henry Rudolph Immerwahr Distinguished Professor of Sociology. He has written more than 100 academic papers and two books, including Structural Equations with Latent Variables (Wiley).
The University of North Carolina at Chapel Hill’s L. L. Thurstone Psychometric Laboratory is home to Associate Professor of Psychology Patrick J. Curran, PhD. Through his integrated program of research, writing, and teaching, he has contributed to the creation and use of new quantitative approaches in the social sciences.
