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Produktbild: Latent Class Analysis

Latent Class Analysis

Aus der Reihe Wiley Series in Survey Methodology

144,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.12.2010

Abbildungen

Charts: 4 B&W, 0 Color; Drawings: 26 B&W, 0 Color; Screen captures: 19 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 6 B&W, 0 Color

Verlag

John Wiley & Sons

Seitenzahl

412

Maße (L/B/H)

24/16,1/2,7 cm

Gewicht

783 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-28907-5

Beschreibung

Rezension

"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)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

03.12.2010

Abbildungen

Charts: 4 B&W, 0 Color; Drawings: 26 B&W, 0 Color; Screen captures: 19 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 6 B&W, 0 Color

Verlag

John Wiley & Sons

Seitenzahl

412

Maße (L/B/H)

24/16,1/2,7 cm

Gewicht

783 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-0-470-28907-5

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Latent Class Analysis
  • 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 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