Latent Class and Latent Transition Analysis
Beschreibung
On a daily basis, researchers in the social, behavioral, andhealth sciences collect information and fit statistical models tothe gathered empirical data with the goal of making significantadvances in these fields. In many cases, it can be useful toidentify latent, or unobserved, subgroups in a population, whereindividuals' subgroup membership is inferred from their responseson a set of observed variables. Latent Class and LatentTransition Analysis provides a comprehensive and unifiedintroduction to this topic through one-of-a-kind, step-by-steppresentations and coverage of theoretical, technical, and practicalissues in categorical latent variable modeling for bothcross-sectional and longitudinal data.
The book begins with an introduction to latent class and latenttransition analysis for categorical data. Subsequent chapters delveinto more in-depth material, featuring:
* A complete treatment of longitudinal latent class models
* Focused coverage of the conceptual underpinnings ofinterpretation and evaluationof a latent class solution
* Use of parameter restrictions and detection of identificationproblems
* Advanced topics such as multi-group analysis and the modelingand interpretation of interactions between covariates
The authors present the topic in a style that is accessible yetrigorous. Each method is presented with both a theoreticalbackground and the practical information that is useful for anydata analyst. Empirical examples showcase the real-worldapplications of the discussed concepts and models, and each chapterconcludes with a "Points to Remember" section that contains a briefsummary of key ideas. All of the analyses in the book are performedusing Proc LCA and Proc LTA, the authors' own software packagesthat can be run within the SAS® environment. A related Website houses information on these freely available programs and thebook's data sets, encouraging readers to reproduce the analyses andalso try their own variations.
Latent Class and Latent Transition Analysis is anexcellent book for courses on categorical data analysis and latentvariable models at the upper-undergraduate and graduate levels. Itis also a valuable resource for researchers and practitioners inthe social, behavioral, and health sciences who conduct latentclass and latent transition analysis in their everyday work.
Weitere Details
Weitere Ausgaben
Personen
STEPHANIE T. LANZA, PhD, is Scientific Director andSenior Research Associate at The Methodology Center at ThePennsylvania State University. She currently focuses her researchon latent class and latent transition analysis and theirapplications in the social, behavioral, and health sciences.
Inhalt
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.
