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Latent Class and Latent Transition Analysis Applications in the Social, Behavioral, and Health Sciences [Other digital carrier]

(The Pennsylvania State University), (The Pennsylvania State University)
  • Formaat: Other digital carrier, 330 pages, kõrgus x laius x paksus: 250x150x15 mm, kaal: 666 g
  • Ilmumisaeg: 19-Jan-2010
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470567333
  • ISBN-13: 9780470567333
Teised raamatud teemal:
Latent Class and Latent Transition Analysis Applications in the Social, Behavioral, and Health Sciences
  • Formaat: Other digital carrier, 330 pages, kõrgus x laius x paksus: 250x150x15 mm, kaal: 666 g
  • Ilmumisaeg: 19-Jan-2010
  • Kirjastus: Wiley-Blackwell
  • ISBN-10: 0470567333
  • ISBN-13: 9780470567333
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470567333.html
Märksõnad:
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.

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.

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. 4.8
Standard errors. 4.9 Suggested supplemental readings. 4.10 Points to
remember. 4.11 What's next. Part II Advanced LCA.
5. Multiple-group LCA.
5.1 Overview. 5.2 Introduction. 5.3 Multiple-group LCA: Model and notation.
5.4 Computing the number of parameters estimated. 5.5 Expressing group
differences in the LCA model. 5.6 Measurement invariance. 5.7 Establishing
whether the number of latent classes is identical across groups. 5.8
Establishing invariance of item-response probabilities across groups. 5.9
Interpretation when measurement invariance does not hold. 5.10 Strategies
when measurement invariance does not hold. 5.11 Significant differences and
important differences. 5.12 Testing equivalence of latent class prevalences
across groups. 5.13 Suggested supplemental readings. 5.14 Points to remember.
5.15 What's next.
6. LCA with Covariates. 6.1 Overview. 6.2 Empirical
example: Positive health behaviors. 6.3 Preparing to conduct LCA with
covariates. 6.4 LCA with covariates: Model and notation. 6.5 Hypothesis
testing in LCA with covariates. 6.6 Interpretation of the intercepts and
regression coefficients. 6.7 Empirical examples of LCA with a single
covariate. 6.8 Empirical example of multiple covariates and interaction
terms. 6.9 Multiple-group LCA with covariates: Model and notation. 6.10
Grouping variable or covariate? 6.11 Use of a Bayesian prior to stabilize
estimation. 6.12 Binomial logistic regression. 6.13 Suggested supplemental
readings. 6.14 Points to remember. 6.15 What's next. Part III Latent Class
Models for Longitudinal Data.
7. RMLCA and LTA. 7.1 Overview. 7.2 RMLCA.
7.3 LTA. 7.4 LTA model parameters. 7.5 LTA: Model and notation. 7.6 Degrees
of freedom associated with latent transition models. 7.7 Empirical example:
Adolescent depression. 7.8 Empirical example: Dating and sexual risk
behavior. 7.9 Interpreting what a latent transition model reveals about
change. 7.10 Parameter restrictions in LTA. 7.11 Testing the hypotheses of
measurement invariance across times. 7.12 Testing the hypotheses about change
between times. 7.13 Relation between RMLCA and LTA. 7.14 Invariance of the
transition probability matrix. 7.15 Suggested supplemental readings. 7.16
Points to remember. 7.17 What's next.
8. Multiple-Group LTA and LTA with
Covariates. 8.1 Overview. 8.2 LTA with a grouping variable. 8.3
Multiple-group LTA: Model and notation. 8.4 Computing the number of
parameters estimated in multiple-group latent transition models. 8.5
Hypothesis tests concerning group differences: General consideration. 8.6
Overall hypothesis tests about group differences in LTA. 8.7 Testing the
hypothesis of equality of latent status prevalences. 8.8 Testing the
hypothesis of equality of transition probabilities. 8.9 Incorporating
covariates in LTA. 8.10 LTA with covariates: Model and notation. 8.11
Hypothesis testing in LTA with covariates. 8.12 Including both a grouping
variable and a covariate in LTA. 8.13 Binomial logistic regression. 8.14 The
relation between multiple-group LTA and LTA with a covariate. 8.15 Suggested
supplemental readings. 8.16 Points to remember. Topic Index. Author Index.
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.