FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W593310612> ?p ?o ?g. }

• W593310612 abstract "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 Hui Walter 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 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 Analysis. 5. Further Aspects of Latent Class Modeling. 5.1 Parameter Estimation. 5.1.1 Simulation and 5.1.2 Model Identifi ability. 5.1.3 Checking Identifi ability with Expeculation. 5.1.4 Data Sparseness. 5.1.5 Boundary Estimates. 5.1.6 Local Maxima. 5.1.7 Latent Class Flippage. 5.2 Local Dependence Models. 5.2.1 Unexplained Heterogeneity. 5.2.2 Correlated Errors. 5.2.3 Bivocality. 5.2.4 A Strategy for Modeling Local Dependence. 5.2.5 Example: Locally Dependent Measures of Sexual Assault. 5.3 Modeling Complex Survey Data. 5.3.1 Objectives of Survey Weighting. 5.3.2 LCA with Complex Survey Data. 5.3.3 Including Design Variables in the Fitted Model. 5.3.4 Weighted and Rescaled Frequencies. 5.3.5 Pseudo-Maximum-Likelihood Estimation. 5.3.6 Treating the Average Cell Weight as an Offset Parameter. 5.3.7 Two-Step Estimation. 5.3.8 Illustration of Weighted and Unweighted Analyses. 5.3.9 Conclusions and Recommendations. 6. Latent Class Models for Special Applications. 6.1 Models for Ordinal Data. 6.2 A Latent Class Model for Reliability. 6.2.1 Generalized Kappa Statistics. 6.2.2 Comparison of Error Model and Agreement Model Concepts of Reliability. 6.2.3 Reliability of Self-Reports of Race. 6.3 Capture Recapture Models. 6.3.1 Latent Class Capture Recapture Models. 6.3.2 Modeling Erroneous Enumerations. 6.3.3 Parameter Estimation. 6.3.4 Example: Evaluating the Census Undercount. 6.3.5 Example: Classifi cation Error in a PES. 7. Latent Class Models for Panel Data. 7.1 Markov Latent Class Models. 7.1.1 Manifest Markov Models. 7.1.2 Example: Application of the MM Model to Labor Force Data. 7.1.3 Markov Latent Class Models. 7.1.4 Example: Application of the MLC Model to Labor Force Data. 7.1.5 The EM Algorithm for MLC Models. 7.1.6 MLC Model with Grouping Variables. 7.1.7 Example: CPS Labor Force Status Classification Error. 7.1.8 Example: Underreporting in Consumer Expenditure Survey. 7.2 Some Nonstandard Markov Models. 7.2.1 Manifest Mover Stayer Model. 7.2.2 Latent Class Mover Stayer Model. 7.2.3 Second-Order MLC Model. 7.2.4 Example: CEIS Analysis with Four Timepoints. 7.2.5 MLC Model with Time-Varying Grouping Variables. 7.2.6 Example: Assessment of Subject Interests. 7.2.7 Multiple Indicators at One or More Waves. 7.3 Further Aspects of Markov Latent Class Analysis. 7.3.1 Estimation Issues with MLCA. 7.3.2 Methods for Panel Nonresponse. 7.3.3 Example: Assessment of Subject Interests with Nonresponse. 8. Survey Error Evaluation: Past, Present, and Future. 8.1 History of Survey Error Evaluation Methodology. 8.1.1 The US Census Bureau Model for Survey Error. 8.1.2 From Bross Model to the Standard LC and MLC Models. 8.1.3 Loglinear Models with Latent Variables. 8.2 Current State of the Art. 8.2.1 Criticisms of LCA for Survey Error Evaluation. 8.2.2 General Strategy for Applying LC and MLC Models. 8.3 Some Ideas for Future Directions. 8.4 Conclusions. Appendix A. Two-Stage Sampling Formulas. Appendix B. Loglinear Modeling Essentials. B.1 Loglinear versus ANOVA Models: Similarities and Differences. B.2 Modeling Cell and Other Conditional Probabilities. B.3 Generalization to Three Variables. B.4 Estimation of Loglinear and Logit Models. References. Index." @default.
• W593310612 created "2016-06-24" @default.
• W593310612 creator A5043964505 @default.
• W593310612 date "2011-01-25" @default.
• W593310612 modified "2023-09-27" @default.
• W593310612 title "Latent Class Analysis of Survey Error" @default.
• W593310612 cites W142604983 @default.
• W593310612 cites W1983683733 @default.
• W593310612 cites W2054072145 @default.
• W593310612 hasPublicationYear "2011" @default.
• W593310612 type Work @default.
• W593310612 sameAs 593310612 @default.
• W593310612 citedByCount "24" @default.
• W593310612 countsByYear W5933106122012 @default.
• W593310612 countsByYear W5933106122013 @default.
• W593310612 countsByYear W5933106122014 @default.
• W593310612 countsByYear W5933106122015 @default.
• W593310612 countsByYear W5933106122016 @default.
• W593310612 countsByYear W5933106122018 @default.
• W593310612 countsByYear W5933106122020 @default.
• W593310612 countsByYear W5933106122021 @default.
• W593310612 crossrefType "book" @default.
• W593310612 hasAuthorship W593310612A5043964505 @default.
• W593310612 hasConcept C105795698 @default.
• W593310612 hasConcept C106906290 @default.
• W593310612 hasConcept C121332964 @default.
• W593310612 hasConcept C139945424 @default.
• W593310612 hasConcept C163258240 @default.
• W593310612 hasConcept C171606756 @default.
• W593310612 hasConcept C179024874 @default.
• W593310612 hasConcept C19619285 @default.
• W593310612 hasConcept C19875794 @default.
• W593310612 hasConcept C207968926 @default.
• W593310612 hasConcept C33923547 @default.
• W593310612 hasConcept C43214815 @default.
• W593310612 hasConcept C62520636 @default.
• W593310612 hasConceptScore W593310612C105795698 @default.
• W593310612 hasConceptScore W593310612C106906290 @default.
• W593310612 hasConceptScore W593310612C121332964 @default.
• W593310612 hasConceptScore W593310612C139945424 @default.
• W593310612 hasConceptScore W593310612C163258240 @default.
• W593310612 hasConceptScore W593310612C171606756 @default.
• W593310612 hasConceptScore W593310612C179024874 @default.
• W593310612 hasConceptScore W593310612C19619285 @default.
• W593310612 hasConceptScore W593310612C19875794 @default.
• W593310612 hasConceptScore W593310612C207968926 @default.
• W593310612 hasConceptScore W593310612C33923547 @default.
• W593310612 hasConceptScore W593310612C43214815 @default.
• W593310612 hasConceptScore W593310612C62520636 @default.
• W593310612 hasLocation W5933106121 @default.
• W593310612 hasOpenAccess W593310612 @default.
• W593310612 hasPrimaryLocation W5933106121 @default.
• W593310612 hasRelatedWork W138052739 @default.
• W593310612 hasRelatedWork W1490482352 @default.
• W593310612 hasRelatedWork W1543245288 @default.
• W593310612 hasRelatedWork W1579271636 @default.
• W593310612 hasRelatedWork W1600596530 @default.
• W593310612 hasRelatedWork W1604544535 @default.
• W593310612 hasRelatedWork W1873057782 @default.
• W593310612 hasRelatedWork W1922709871 @default.
• W593310612 hasRelatedWork W2039404010 @default.
• W593310612 hasRelatedWork W2049633694 @default.
• W593310612 hasRelatedWork W2053325424 @default.
• W593310612 hasRelatedWork W2093268197 @default.
• W593310612 hasRelatedWork W2115881827 @default.
• W593310612 hasRelatedWork W2155591630 @default.
• W593310612 hasRelatedWork W2325866568 @default.
• W593310612 hasRelatedWork W2481530215 @default.
• W593310612 hasRelatedWork W2505318372 @default.
• W593310612 hasRelatedWork W2554987453 @default.
• W593310612 hasRelatedWork W3042168128 @default.
• W593310612 hasRelatedWork W3161517006 @default.
• W593310612 isParatext "false" @default.
• W593310612 isRetracted "false" @default.
• W593310612 magId "593310612" @default.
• W593310612 workType "book" @default.