Latent Class Analysis of Survey Error

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 lEM software to replicate the presented analyses. Appendices
supply a primer on categorical data analysis, and a related Web
site houses the lEM 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.

Autorentext
Paul P. Biemer, PhD, is Distinguished Fellow in Statistics at RTI International and Associate Director for Survey Research and Development at the Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. An expert in the field of survey measurement error, Dr. Biemer has published extensively in his areas of research interest, which include survey design and analysis; general survey methodology; and nonsampling error modeling and evaluation. He is a coauthor of Introduction to Survey Quality and a coeditor of Telephone Survey Methodology, Survey Measurement and Process Quality, and Measurement Errors in Surveys, all published by Wiley.

Zusammenfassung
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 lEM software to replicate the presented analyses. Appendices supply a primer on categorical data analysis, and a related Web site houses the lEM 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.

Inhalt
Preface. Abbreviations.

1. Survey Error Evaluation.

1.1 Survey Error.

1.1.1 An Overview of Surveys.

1.1.2 Survey Quality and Accuracy and Total Survey Error.

1.1.3 Nonsampling Error.

1.2 Evaluating the Mean-Squared Error.

1.2.1 Purposes of MSE Evaluation.

1.2.2 Effects of Nonsampling Errors on Analysis.

1.2.3 Survey Error Evaluation Methods.

1.2.4 Latent Class Analysis.

1.3 About This Book.

2. A General Model for Measurement Error.

2.1 The Response Distribution.

2.1.1 A Simple Model of the Response Process.

2.1.2 The Reliability Ratio.

2.1.3 Effects of Response Variance on Statistical Inference.

2.2 Variance Estimation in the Presence of Measurement Error.

2.2.1 Binary Response Variables.

2.2.2 Special Case: Two Measurements.

2.2.3 Extension to Polytomous Response Variables.

2.3 Repeated Measurements.

2.3.1 Designs for Parallel Measurements.

2.3.2 Nonparallel Measurements.

2.3.3 Example: Reliability of Marijuana Use Questions.

2.3.4 Designs Based on a Subsample.

2.4 Reliability of Multiitem Scales.

2.4.1 Scale Score Measures.

2.4.2 Cronbach's Alpha.

2.5 True Values, Bias, and Validity.

2.5.1 A True Value Model.

2.5.2 Obtaining True Values.

2.5.3 Example: Poor- or Failing-Grade Data.

3. Response Probability Models for Two Measurements.

3.1 Response Probability Model.

3.1.1 Bross' Model.

3.1.2 Implications for Survey Quality Investigations.

3.2 Estimating , , and .

3.2.1 Maximum-Likelihood Estimates of , , and .

3.2.2 The EM Algorithm for Two Measurements.

3.3 HuiWalter Model for Two Dichotomous Measurements.

3.3.1 Notation and Assumptions.

3.3.2 Example: Labor Force Misclassifi cations.

3.3.3 Example: Mode of Data Collection Bias.

3.4 Further Aspects of the HuiWalter Model.

3.4.1 Two Polytomous Measurements.

3.4.2 Example: Misclassifi cation with Three Categories.

3.4.3 Sensitivity of the HuiWalter Method to Violations in the Underlying Assumptions.

3.4.4 HuiWalter Estimates of Reliability.

3.5 Three or More Polytomous Measurements.

4. Latent Class Models for Evaluating Classifi cation Errors.

4.1 The Standard Latent Class Model.

4.1.1 Latent Variable Models.

4.1.2 An Example from Typology Analysis.

4.1.3 Latent Class Analysis Software.

4.2 Latent Class Modeling Basics.

4.2.1 Model Assumptions.

4.2.2 Probability Model Parameterization of the Standard LC Model.

4.2.3 Estimation of the LC Model Parameters.

4.2.4 Loglinear Model Parameterization.

4.2.5 Example: Computing Probabilities Using Loglinear Parameters.

4.2.6 Modifi ed Path Model Parameterization.

4.2.7 Recruitment Probabilities.

4.2.8 …