Combining theoretical, methodological, and practical aspects,
Latent Class Analysis of Survey Error successfully guides readers
through the accurate interpretation of survey results for quality
evaluation and improvement. This book is a comprehensive resource
on the key statistical tools and techniques employed during the
modeling and estimation of classification errors, featuring a
special focus on both latent class analysis (LCA) techniques and
models for categorical dat...
Combining theoretical, methodological, and practical aspects,
Latent Class Analysis of Survey Error successfully guides readers
through the accurate interpretation of survey results for quality
evaluation and improvement. This book is a comprehensive resource
on the key statistical tools and techniques employed during the
modeling and estimation of classification errors, featuring a
special focus on both latent class analysis (LCA) techniques and
models for categorical data from complex sample surveys.
Drawing from his extensive experience in the field of survey
methodology, the author examines early models for survey
measurement error and identifies their similarities and differences
as well as their strengths and weaknesses. Subsequent chapters
treat topics related to modeling, estimating, and reducing errors
in surveys, including:
* Measurement error modeling forcategorical data
* The Hui-Walter model and othermethods for two indicators
* The EM algorithm and its role in latentclass model parameter
estimation
* Latent class models for three ormore indicators
* Techniques for interpretation of modelparameter estimates
* Advanced topics in LCA, including sparse data, boundary values,
unidentifiability, and local maxima
* Special considerations for analyzing datafrom clustered and
unequal probability samples with nonresponse
* The current state of LCA and MLCA (multilevel latent class
analysis), and an insightful discussion on areas for further
research
Throughout the book, more than 100 real-world examples describe
the presented methods in detail, and readers are guided through the
use of l EM software to replicate the presented analyses. Appendices
supply a primer on categorical data analysis, and a related Web
site houses the l EM software.
Extensively class-tested to ensure an accessible presentation,
Latent Class Analysis of Survey Error is an excellent book for
courses on measurement error and survey methodology at the graduate
level. The book also serves as a valuable reference for researchers
and practitioners working in business, government, and the social
sciences who develop, implement, or evaluate surveys.
