Latent Class Analysis of Survey Error
(Sprache: Englisch)
This book concerns the error in data collected using sample surveys, the nature and magnitudes of the errors, their effects on survey estimates, how to model and estimate the errors using a variety of modeling methods, and, finally, how to interpret the...
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Produktinformationen zu „Latent Class Analysis of Survey Error “
This book concerns the error in data collected using sample surveys, the nature and magnitudes of the errors, their effects on survey estimates, how to model and estimate the errors using a variety of modeling methods, and, finally, how to interpret the estimates and make use of the results in reducing the error for future surveys.
Klappentext zu „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
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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.
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Inhaltsverzeichnis zu „Latent Class Analysis of Survey Error “
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 À, ¸, and Æ.
3.2.1 Maximum-Likelihood Estimates of À, ¸, and Æ.
3.2.2 The EM Algorithm for Two Measurements.
3.3 Hui-Walter Model for Two Dichotomous Measurements.
3.3.1 Notation and Assumptions.
3.3.2 Example: Labor Force Misclassifi
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cations.
3.3.3 Example: Mode of Data Collection Bias.
3.4 Further Aspects of the Hui-Walter Model.
3.4.1 Two Polytomous Measurements.
3.4.2 Example: Misclassifi cation with Three Categories.
3.4.3 Sensitivity of the Hui-Walter Method to Violations in the Underlying Assumptions.
3.4.4 Hui-Walter 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 Example: Computing Probabilities Using Modified Path Model Parameters.
4.3 Incorporating Grouping Variables.
4.3.1 Example: Loglinear Parameterization of the Hui-Walter Model.
4.3.2 Example: Analysis of Past-Year Marijuana Use with Grouping Variables.
4.4 Model Estimation and Evaluation.
4.4.1 EM Algorithm for the LL Parameterization.
4.4.2 Assessing Model Fit.
4.4.3 Model Selection.
4.4.4 Model-Building Strategies.
4.4.5 Model Restrictions.
4.4.6 Example: Continuation of Marijuana Use A
3.3.3 Example: Mode of Data Collection Bias.
3.4 Further Aspects of the Hui-Walter Model.
3.4.1 Two Polytomous Measurements.
3.4.2 Example: Misclassifi cation with Three Categories.
3.4.3 Sensitivity of the Hui-Walter Method to Violations in the Underlying Assumptions.
3.4.4 Hui-Walter 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 Example: Computing Probabilities Using Modified Path Model Parameters.
4.3 Incorporating Grouping Variables.
4.3.1 Example: Loglinear Parameterization of the Hui-Walter Model.
4.3.2 Example: Analysis of Past-Year Marijuana Use with Grouping Variables.
4.4 Model Estimation and Evaluation.
4.4.1 EM Algorithm for the LL Parameterization.
4.4.2 Assessing Model Fit.
4.4.3 Model Selection.
4.4.4 Model-Building Strategies.
4.4.5 Model Restrictions.
4.4.6 Example: Continuation of Marijuana Use A
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Autoren-Porträt von Paul P. Biemer
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.
Bibliographische Angaben
- Autor: Paul P. Biemer
- 2010, 1. Auflage, 412 Seiten, Maße: 16,5 x 24,4 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470289074
- ISBN-13: 9780470289075
Sprache:
Englisch
Rezension zu „Latent Class Analysis of Survey Error “
"Biemer (statistics, RTI International and survey research and development, U. of North Carolina at Chapel Hill) provides a comprehensive source on the primary statistical tools and techniques used in the modeling and estimation of classification errors, with a particular focus on latent class techniques and models for categorical data from complex sample surveys . . . the book would be useful as a text for graduate level courses in measurement error and survey methodology, as well as a reference for researchers and professionals in business, government, and social sciences who are responsible for developing, implementing, or evaluating surveys." (Booknews, 1 April 2011)"By combining theoretical, methodological and practical aspects of estimating classification error, the book provides a guide for the practitioner as well as a text for the student of survey error evaluation". (RTI International, 18 January 2011)
Pressezitat
"Biemer (statistics, RTI International and survey research and development, U. of North Carolina at Chapel Hill) provides a comprehensive source on the primary statistical tools and techniques used in the modeling and estimation of classification errors, with a particular focus on latent class techniques and models for categorical data from complex sample surveys . . . the book would be useful as a text for graduate level courses in measurement error and survey methodology, as well as a reference for researchers and professionals in business, government, and social sciences who are responsible for developing, implementing, or evaluating surveys." (Booknews, 1 April 2011)"By combining theoretical, methodological and practical aspects of estimating classification error, the book provides a guide for the practitioner as well as a text for the student of survey error evaluation". (RTI International, 18 January 2011)
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