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Johnson, Valen E. [Verfasser:in] ; Albert, Jim [Sonstige Person, Familie und Körperschaft]

Ordinal Data Modeling

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  • Medientyp: E-Book
  • Titel: Ordinal Data Modeling
  • Enthält: ""Preface""; ""Contents""; ""Review of Classical and Bayesian Inference""; ""Learning about a binomial proportion""; ""Inference for a normal mean""; ""Inference about a set of proportions""; ""Further reading""; ""Exercises""; ""Review of Bayesian Computation""; ""Integrals, integrals, integrals, ""; ""An example""; ""Non-Simulation-Based Algorithms""; ""Direct Simulation""; ""Markov Chain Monte Carlo""; ""A two-stage exchangeable model""; ""Further reading""; ""Appendix: Iterative implementation of Gauss- Hermite quadrature""; ""Exercises""; ""Regression Models for Binary Data""
    ""Basic modeling considerations""""Estimating binary regression coefficients""; ""Latent variable interpretation of binary regression""; ""Residual analysis and goodness of fit""; ""An example""; ""A note on retrospective sampling and logistic regression""; ""Further reading""; ""Appendix: iteratively reweighted least squares""; ""Exercises""; ""Regression Models for Ordinal Data""; ""Ordinal data via latent variables""; ""Parameter constraints and prior models""; ""Estimation strategies""; ""Residual analysis and goodness of fit""; ""Examples""
    ""Prediction of essay scores from grammar attributes""""Further reading""; ""Appendix: iteratively reweighted least squares""; ""Exercises""; ""Analyzing Data from Multiple Raters""; ""Essay scores from five raters""; ""The multiple rater model""; ""Incorporating regression functions into multirater data""; ""ROC analysis""; ""Further reading""; ""Exercises""; ""Item Response Modeling""; ""Introduction""; ""Modeling the probability of a correct response""; ""Modeling test results for a group of examinees""; ""Classical estimation of item and ability parameters""
    ""Bayesian estimation of item parameters""""Estimation of model parameters (probit link)""; ""An example""; ""One-parameter (item response) models""; ""Three-parameter item response models""; ""Model checking""; ""An exchangeable model""; ""Further reading""; ""Exercises""; ""Graded Response Models: A Case Study of Undergraduate Grade Data""; ""Background""; ""A Bayesian graded response model""; ""Parameter estimation""; ""Applications""; ""Alternative models and sensitivity analysis""; ""Discussion""; ""Appendix: selected transcripts of Duke University undergraduates""
    ""Appendix: Software for Ordinal Data Modeling""""An Introduction to MATLAB""; ""Chapter 2 � Review ofBayesian computation""; ""Chapter 3 � Regression models for binary data""; ""Chapter 4 � Regression models for ordinal data""; ""Chapter 5 � Analyzing data from multiple raters""; ""Chapter 6 � Item response modeling""; ""References""; ""Index""
  • Beteiligte: Johnson, Valen E. [Verfasser:in]; Albert, Jim [Sonstige Person, Familie und Körperschaft]
  • Erschienen: New York, NY: Springer-Verlag New York, Inc, 1999
  • Erschienen in: Statistics for Social Science and Behavorial Sciences
    Statistics for Social and Behavioral Sciences
    SpringerLink ; Bücher
  • Umfang: Online-Ressource (X, 258 p, online resource)
  • Sprache: Englisch
  • DOI: 10.1007/b98832
  • ISBN: 9780387227023
  • Identifikator:
  • Schlagwörter: Sozialwissenschaften > Statistik
    Politische Wissenschaft > Statistik
  • Entstehung:
  • Anmerkungen: Includes bibliographical references (p. [249]-254) and index
  • Beschreibung: Review of Classical and Bayesian Inference -- Review of Bayesian Computation -- Regression Models for Binary Data -- Regression Models for Ordinal Data -- Analyzing Data from Multiple Raters -- Item Response Modeling -- Graded Response Models: A Case Study of Undergraduate Grade Data.

    Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. Written for graduate students and researchers in the statistical and social sciences, this book describes a coherent framework for understanding binary and ordinal regression models, item response models, graded response models, and ROC analyses, and for exposing the close connection between these models. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.