Analysis of Ordinal DataAnalysis of Ordinal Data



Ordinal data can be rank ordered but not assumed to have equal distances between categories. Using support by judges for civil rights measures and bussing as the primary example, this paper indicates how such data can best be analyzed.

Author: David K. Hildebrand

Publisher: SAGE

ISBN: 0803907958

Category:

Page: 79

View: 666

Ordinal data can be rank ordered but not assumed to have equal distances between categories. Using support by judges for civil rights measures and bussing as the primary example, this paper indicates how such data can best be analyzed.

Analysis of Ordinal Categorical DataAnalysis of Ordinal Categorical Data



Additional features of this Second Edition include: A new chapter on marginal models for multivariate ordinal responses, using maximum likelihood and generalized estimating equations for model fitting A new chapter on random effects models ...

Author: Alan Agresti

Publisher: John Wiley & Sons

ISBN: 9781118209998

Category:

Page: 424

View: 964

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.

Analysis of Ordinal Categorical DataAnalysis of Ordinal Categorical Data



The models and measures of association for ordinal data presented in this book bear many resemblances to those for continuous variables. A major theme of this book is how to analyze ordinal data by utilizing their quantitative nature.

Author: Alan Agresti

Publisher: John Wiley & Sons

ISBN: 9780470082898

Category:

Page: 424

View: 299

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.

Statistical Models for Ordinal VariablesStatistical Models for Ordinal Variables



"This book provides an outstanding introduction to. . . using association models developed primarily by Leo Goodman.

Author: Clifford C. Clogg

Publisher: SAGE Publications, Incorporated

ISBN: UOM:39015032895842

Category:

Page: 192

View: 363

"This book provides an outstanding introduction to. . . using association models developed primarily by Leo Goodman. . . . This well-written book provides a careful and generally clear introduction to association models. . . . the authors have achieved their aims well. They make a strong case for the usefulness of association models in a variety of applications. Clogg. . . and Shihadeh have provided sociologists with an introduction filled with wise advice about analyzing associations between ordinal variables." --Alan Agresti in Contemporary Sociology "This is a very useful book about. . . statistical models for ordinal variables. Reading this book. . . your reviewer was pleased to find a clear and succinct account explaining a variety of association models. . . . These models are the 'RC' models. . . . it is to statistical methods for the social sciences that this book. . . is aimed. . . . This is not a total beginner's book, however. . . and I thought the pace a little faster than leisurely. . . . a fine resource of clear description and explanation of the use of statistical models for ordinal data. . . ." --M. C. Jones in Journal of the Royal Statistical Society "This book is worthwhile reading for statisticians who have scattered training in ordinal data analysis and want to pull this training into a coherent overview. It is a fine supplement to other more mathematical books in the area. . . . After reading the book, the reader will have a clear understanding of the role of odds ratios in ordinal data analysis." --Technometrics "Includes a concise but clear review of criteria for assessing goodness-of-fit. . . . I found this volume an accessible unification of work in the area. I recommend it." --International Statistical Institute How should data involving response variables of many ordered categories be analyzed? What technique is the most useful in analyzing partially ordered variables regarded as dependent variables? Addressing these and other related concerns in social and survey research, this book carefully explores the statistical analysis of data involving dependent variables that can be coded into discrete, ordered categories. Through an analysis of ordinal variables, the authors cover the general procedures for assessing goodness-of-fit, review the independence model and the saturated model, define measures of association, demonstrate the logit version of the model and the jackknife method for contingency tables, and explain associated models for two-way tables as well as logit-type regression models.

Multi layer Perceptron Networks for Ordinal Data AnalysisMulti layer Perceptron Networks for Ordinal Data Analysis



This book addresses researchers, lecturers and students of mathematics, informatics and artificial intelligence. It may also be interesting for those who deal with data analysis in their daily work.

Author: Stephan Dlugosz

Publisher:

ISBN: 383251984X

Category:

Page: 280

View: 718

Whether the results of statistical procedures are accepted or not is strongly influenced by the way how they are interpreted. Effects based on data encodings and the order in which the data occurs in the learning sample are particularly problematic. Modern data collections often contain large numbers of items, each including many variables. These variables are usually measured on different scales among which the ordinal scale is the most common. Versatile and efficient data analysis models are required for mining these data. Multi-layer Perceptron (MLP) Networks are very flexible models for analyzing problems that have an input-output structure. These techniques are well-known in artificial intelligence and provide models for non-linear statistical regression and classification with efficient learning algorithms. The author of this thesis develops extensions to MLP networks suitable for the appropriate analysis of ordinal data occurring both as inputs and outputs. Reviewing the learning procedure he introduces a new learning paradigm that combines the advantages of batch learning on the one hand and incremental estimation on the other, i.e. statistically better results and algorithmic efficiency respectively. This allows an efficient online adaptation of the model without being compromised by the dependence on either a learning parameter or the ordering of the data set. This book addresses researchers, lecturers and students of mathematics, informatics and artificial intelligence. It may also be interesting for those who deal with data analysis in their daily work.