A modern, comprehensive treatment of latent class and latent
transition analysis for categorical data
On a daily basis, researchers in the social, behavioral, and
health sciences collect information and fit statistical models to
the gathered empirical data with the goal of making significant
advances in these fields. In many cases, it can be useful to
identify latent, or unobserved, subgroups in a population, where
individuals' subgroup membership is inferred from their responses
on a set of observed variables. Latent Class and Latent
Transition Analysis provides a comprehensive and unified
introduction to this topic through one-of-a-kind, step-by-step
presentations and coverage of theoretical, technical, and practical
issues in categorical latent variable modeling for both
cross-sectional and longitudinal data.
The book begins with an introduction to latent class and latent
transition analysis for categorical data. Subsequent chapters delve
into more in-depth material, featuring:
* A complete treatment of longitudinal latent class models
* Focused coverage of the conceptual underpinnings of
interpretation and evaluationof a latent class solution
* Use of parameter restrictions and detection of identification
problems
* Advanced topics such as multi-group analysis and the modeling
and interpretation of interactions between covariates
The authors present the topic in a style that is accessible yet
rigorous. Each method is presented with both a theoretical
background and the practical information that is useful for any
data analyst. Empirical examples showcase the real-world
applications of the discussed concepts and models, and each chapter
concludes with a "Points to Remember" section that contains a brief
summary of key ideas. All of the analyses in the book are performed
using Proc LCA and Proc LTA, the authors' own software packages
that can be run within the SAS® environment. A related Web
site houses information on these freely available programs and the
book's data sets, encouraging readers to reproduce the analyses and
also try their own variations.
Latent Class and Latent Transition Analysis is an
excellent book for courses on categorical data analysis and latent
variable models at the upper-undergraduate and graduate levels. It
is also a valuable resource for researchers and practitioners in
the social, behavioral, and health sciences who conduct latent
class and latent transition analysis in their everyday work.
Autorentext
Linda M. Collins, PhD, is Director of The Methodology Center and Professor of Human Development and Family Studies at The Pennsylvania State University. A Fellow of the American Psychological Association and the Association for Psychological Science, Dr. Collins has published numerous journal articles in her areas of research interest, which include experimental and non-experimental design and models for longitudinal data.
STEPHANIE T. LANZA, PhD, is Scientific Director and Senior Research Associate at The Methodology Center at The Pennsylvania State University. She currently focuses her research on latent class and latent transition analysis and their applications in the social, behavioral, and health sciences.
Klappentext
A modern, comprehensive treatment of latent class and latent transition analysis for categorical data
On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data.
The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring:
-
A complete treatment of longitudinal latent class models
-
Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution
-
Use of parameter restrictions and detection of identification problems
-
Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates
The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a "Points to Remember" section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations.
Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.
Inhalt
List of Figures.
List of Tables.
Acknowledgments.
Acronyms.
Part I Fundamentals.
1. General Introduction.
1.1 Overview.
1.2 Conceptual foundation and brief history of the latent class model.
1.3 Why select a categorical latent variable approach?
1.4 Scope of this book.
1.5 Empirical example of LCA: Adolescent delinquency.
1.6 Empirical example of LTA: Adolescent delinquency.
1.7 About this book.
1.8 The examples in this book.
1.9 Software.
1.10 Additional resources: The book's web site.
1.11 Suggested supplemental readings.
1.12 Points to remember.
1.13 What's next.
2. The latent class model.
2.1 Overview.
2.2 Empirical example: Pubertal development.
2.3 The role of item-response probabilities to label the latent classes in the pubertal development example.
2.4 Empirical example: Health risk behaviors.
2.5 LCA: Model and notation.
2.6 Suggested supplemental readings.
2.7 Points to remember.
2.8 What's next.
3. The relation between the latent variable and its indicators.
3.1 Overview.
3.2 The latent class measurement model.
3.3 Homogeneity and latent class separation.
3.4 The precision with which the observed variables measure the latent variable.
3.5 Expressing the degree of uncertainty: Mean posterior probabilities and entropy.
3.6 Points to remember.
3.7 What's next.
4. Parameter estimation and model selection.
4.1 Overview.
4.2 Maximum Likelihood estimation.
4.3 Model fit and model selection.
4.4 Finding the ML solution.
4.5 Empirical example of using many starting values.
4.6 Empirical examples of selecting the number of latent classes.
4.7 More about parameter restrictions.
4.…