## Ordinal Data ModelingOrdinal 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. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

1 | |

6 | 14 |

2 | 16 |

6 | 26 |

Review of Bayesian Computation | 33 |

7 | 41 |

Regression Models for Binary Data | 75 |

7 | 81 |

Analyzing Data from Multiple Raters | 158 |

Item Response Modeling | 182 |

13 | 194 |

A Case Study of Undergraduate Grade | 215 |

Software for Ordinal Data Modeling | 239 |

249 | |

255 | |

Regression Models for Ordinal Data | 126 |

### Other editions - View all

### Common terms and phrases

ˆpi achievement indices analysis assume asymptotic Bayes factor Bayesian residual beta density binary regression models binomial cancer category cutoffs Chapter components computed conditional distribution denote deviance statistic example explanatory variables Figure Find the posterior fitted probabilities Gibbs sampling grade cutoffs graph histogram individual item response curve item response model iterations latent residuals latent traits latent variables likelihood function link function log odds-ratio logistic model marginal likelihood marginal posterior maximum likelihood estimates MCMC algorithm Metropolis-Hastings Metropolis-Hastings algorithm model parameters normal approximation normal distribution observed obtained ordinal data ordinal probit model plot posterior distribution posterior mean posterior probability predictive prior density prior distribution probit model proposal density random effects random variable rater variances regression parameter SAT-M score simulated sample simulated values specified standard deviation standard errors standard normal student success probabilities Suppose Table uniform prior UV index vague prior vector