How should data involving response variables of many ordered categories be analyzed? What technique would be most useful in analyzing partially ordered variables regarded as dependent variables? Addressing these and other related concerns in social and survey research, Clogg and Shihadeh explore the statistical analysis of data involving dependent variables that can be coded into discrete, ordered categories, such as "agree," "uncertain," "disagree," or in other similar ways. The authors emphasize the applications of new models and methods for the analysis of ordinal variables and cover general procedures for assessing goodness-of-fit, review the independence model and the saturated model, define measures of association, demonstrate the logit versions of the model, and develop association models as well as logit-type regression models. Aimed at helping researchers formulate models that take account of the ordering of the levels of the variables, this book is appropriate for readers familiar with log-linear analysis and logit regression.

Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.

Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. 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.

Statistical science’s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy. Analysis of Ordinal Categorical Data, Second Edition provides an introduction to basic descriptive and inferential methods for categorical data, giving thorough coverage of new developments and recent methods. Special emphasis is placed on interpretation and application of methods including an integrated comparison of the available strategies for analyzing ordinal data. Practitioners of statistics in government, industry (particularly pharmaceutical), and academia will want this new edition.

[English]:This volume collects the peer-reviewed contributions presented at the 2nd International Conference on “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 - held at the Department of Political Sciences of the University of Naples Federico II (24-26 October 2018). The Conference brought together theoretical and applied statisticians to share the latest studies and developments in the field. In addition to the fundamental topic of latent structure analysis and modelling, the contributions in this volume cover a broad range of topics including measuring dissimilarity, clustering, robustness, CUB models, multivariate models, and permutation tests. The Conference featured six distinguished keynote speakers: Alan Agresti (University of Florida, USA), Brian Francis (Lancaster University, UK), Bettina Gruen (Johannes Kepler University Linz, Austria), Maria Kateri (RWTH Aachen, Germany), Elvezio Ronchetti (University of Geneva, Switzerland), Gerhard Tutz (Ludwig-Maximilians University of Munich, Germany). The volume includes 22 contributions from scholars that were accepted as full papers for inclusion in this edited volume after a blind review process of two anonymous referees./ [Italiano]: Il volume raccoglie i contributi presentati alla seconda Conferenza Internazionale “Advances in Statistical Modelling of Ordinal Data” - ASMOD 2018 – che si è svolta presso il Dipartimento di Scienze Politiche, Università di Napoli Federico II, nei giorni 24-26 ottobre 2018. La Conferenza ha visto la presentazione di studi sia teorici che applicati al fine di condividere i più recenti sviluppi scientifici nel campo. Oltre al tema fondamentale dell'analisi delle strutture latenti e dei modelli, i contributi richiamano una vasta gamma di argomenti, tra cui misure di dissimilarità, metodi di clustering, analisi di robustezza, modelli CUB, modelli multivariati e test di permutazione. In particolare, questa pubblicazione contiene le relazioni invitate di studiosi riconosciuti a livello internazionale: Alan Agresti (Università della Florida, USA), Brian Francis (Università Lancaster, Regno Unito), Bettina Gruen (Johannes Kepler University Linz, Austria), Maria Kateri (RWTH Aachen, Germania), Elvezio Ronchetti (Università di Ginevra, Svizzera), Gerhard Tutz (Università Ludwig-Maximilians di Monaco, Germania). Il volume include, inoltre, 22 contributi di studiosi che sono stati accettati dopo un processo di revisione anonima.

Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes—all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book.

This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.