„Biemer (statistics, RTI International and survey research anddevelopment, U. of North Carolina at Chapel Hill) provides acomprehensive source on the primary statistical tools andtechniques used in the modeling and estimation of classificationerrors, with a particular focus on latent class techniques andmodels for categorical data from complex sample surveys . . . thebook would be useful as a text for graduate level courses inmeasurement error and survey methodology, as well as a referencefor researchers and professionals in business, government, andsocial sciences who are responsible for developing, implementing, or evaluating surveys.“ (Booknews, 1 April 2011)
„By combining theoretical, methodological and practical aspectsof estimating classification error, the book provides a guide forthe practitioner as well as a text for the student of survey errorevaluation“. (RTI International, 18 January 2011)
Latent Class Analysis of Survey Error
von Paul P. BiemerCombining theoretical, methodological, and practical aspects, Latent Class Analysis of Survey Error successfully guides readersthrough the accurate interpretation of survey results for qualityevaluation and improvement. This book is a comprehensive resourceon the key statistical tools and techniques employed during themodeling and estimation of classification errors, featuring aspecial focus on both latent class analysis (LCA) techniques andmodels for categorical data from complex sample surveys.
Drawing from his extensive experience in the field of surveymethodology, the author examines early models for surveymeasurement error and identifies their similarities and differencesas well as their strengths and weaknesses. Subsequent chapterstreat topics related to modeling, estimating, and reducing errorsin 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 parameterestimation
* 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 andunequal probability samples with nonresponse
* The current state of LCA and MLCA (multilevel latent classanalysis), and an insightful discussion on areas for furtherresearch
Throughout the book, more than 100 real-world examples describethe presented methods in detail, and readers are guided through theuse of lEM software to replicate the presented analyses. Appendicessupply a primer on categorical data analysis, and a related Website houses the lEM software.
Extensively class-tested to ensure an accessible presentation, Latent Class Analysis of Survey Error is an excellent book forcourses on measurement error and survey methodology at the graduatelevel. The book also serves as a valuable reference for researchersand practitioners working in business, government, and the socialsciences who develop, implement, or evaluate surveys.
Drawing from his extensive experience in the field of surveymethodology, the author examines early models for surveymeasurement error and identifies their similarities and differencesas well as their strengths and weaknesses. Subsequent chapterstreat topics related to modeling, estimating, and reducing errorsin 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 parameterestimation
* 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 andunequal probability samples with nonresponse
* The current state of LCA and MLCA (multilevel latent classanalysis), and an insightful discussion on areas for furtherresearch
Throughout the book, more than 100 real-world examples describethe presented methods in detail, and readers are guided through theuse of lEM software to replicate the presented analyses. Appendicessupply a primer on categorical data analysis, and a related Website houses the lEM software.
Extensively class-tested to ensure an accessible presentation, Latent Class Analysis of Survey Error is an excellent book forcourses on measurement error and survey methodology at the graduatelevel. The book also serves as a valuable reference for researchersand practitioners working in business, government, and the socialsciences who develop, implement, or evaluate surveys.
