Analysis of ordinal categorical data by Alan Agresti

Cover of: Analysis of ordinal categorical data | Alan Agresti

Published by Wiley in Hoboken, N.J .

Written in English

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Subjects:

  • Multivariate analysis

Edition Notes

Includes bibliographical references and indexes.

Book details

StatementAlan Agresti
SeriesWiley series in probability and statistics, Wiley series in probability and statistics
Classifications
LC ClassificationsQA278 .A35 2010
The Physical Object
Paginationxi, 396 p. :
Number of Pages396
ID Numbers
Open LibraryOL24802563M
ISBN 100470082895
ISBN 109780470082898
LC Control Number2009038760
OCLC/WorldCa445480016

Download Analysis of ordinal categorical data

Analysis of Ordinal Categorical Data, Second Edition is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It is also an invaluable resource for researchers and practitioners who conduct data analysis in the areas of public health, business, medicine, and the social and behavioral sciences/5(4).

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. Analysis of Ordinal Categorical Data (Wiley Series in Probability and Statistics Book ) - Kindle edition by Agresti, Alan.

Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Analysis of Ordinal Categorical Data (Wiley Series in Probability and Statistics Book )/5(4).

Ordinal data mixes numerical and categorical data. The data fall into categories, but the numbers placed on the categories have meaning. For example, rating a restaurant on a scale from 0 (lowest) to 4 (highest) stars gives ordinal data.

Ordinal data are often treated as categorical, where the groups are ordered when graphs and charts are made. 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. This book has some very useful techniques for analyzing ordinal categorical data (think survey results measured on a scale of, etc.), however, the book is poorly written without enough explanation for some topics/5.

This book, which presents a nontechnical introduction to topics such as logistic regression, is a lower-technical-level and shorter version of the "Categorical Data Analysis" text mentioned above. I've constructed a website for these texts that provides information about the use of Software for Categorical Data Analysis such as SAS, R and S.

Analysis of Ordinal Categorical Data, Second Edition is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels. It is also an invaluable resource for researchers and practitioners who conduct data analysis in the areas of public health, business, medicine, and the social and behavioral sciences/5(10).

"A must-have book for anyone expecting to do research and/or applications in categorical data analysis." —Statistics in Medicine "It is a total delight reading this book." —Pharmaceutical Research "If you do any analysis of categorical data, this is an essential desktop reference." —Technometrics.

analysis of ordinal categorical Analysis of ordinal categorical data book Download analysis of ordinal categorical data or read online books in PDF, EPUB, Tuebl, and Mobi Format. Click Download or Read Online button to get analysis of ordinal categorical data book now.

This site is like a library, Use search box in the widget to get ebook that you want. 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. Website for CATEGORICAL DATA ANALYSIS, 3rd edition For the third edition of Categorical Data Analysis by Alan Agresti (Wiley, ), this site contains (1) information on the use of other software (SAS, R Analysis of ordinal categorical data book S-plus, Stata, SPSS, and others), (2) data sets for examples and many exercises (for many of which, only excerpts were shown in the text itself), (3) short answers for.

Ordinal Modeling Versus Ordinary Regession Analysis 4. Organization of This Book 8. 2 Ordinal Probabilities, Scores, and Odds Ratios 9. Probabilities and Scores for an Ordered Categorical Scale 9. Ordinal Odds Ratios for Contingency Tables Confidence Intervals for Ordinal Association Measures 26Brand: Alan Agresti.

Get this from a library. Analysis of ordinal categorical data. [Alan Agresti] -- The first book to deal comprehensively with specialized methods for categorical data with ordered categories.

It assembles much of the material previously scattered in journals, including very recent. 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 Reviews: 1. Analysis of Ordinal Categorical Data, Second Edition is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels.

It is also an invaluable resource for researchers and practitioners who conduct data analysis in the areas of public health, business, medicine, and the social and behavioral : Agresti.

‎ Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis." — Statistics in Medicine "It is a total delight reading this book." — Pharmaceutical Research "If you.

Analysis of Ordinal Categorical Data, Second Edition, by A. Agresti, Hoboken, NJ: Wiley,ISBNxi + pp., $ This book is the second edition of a text first. Analysis of Ordinal Categorical Data, Second Edition Alan Agresti(auth.) 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.

Book Description. Learn How to Properly Analyze Categorical Data Analysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation.

All this work comes from the GIFI's group book () for multivariate categorical data analysis. Please, note that Gifi is a pseudo for this statistician group, named according to the name of the. Data Analysis Using Loglinear Models.

Loglinear Models for Ordinal Variables. Logit Models. Logit Models for an Ordinal Response. Other Models for Ordinal Variables. Measures of Association for Ordinal Variables. Inference for Ordinal Measures of Association.

Square Tables with Ordered Categories. Comparison of Ordinal Methods. Appendixes. Analysis of Ordinal Categorical Data, Second Edition is an excellent book for courses on categorical data analysis at the upper-undergraduate and graduate levels.

It is also an invaluable resource for researchers and practitioners who conduct data analysis in the areas of public health, business, medicine, and the social and behavioral sciences/5(3).

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.

Categorical Response Data, 1 Response/ExplanatoryVariable Distinction, 2 Nominal/Ordinal Scale Distinction, 2 Organization of this Book, 3 Probability Distributions for Categorical Data, 3 Binomial Distribution, 4 Multinomial Distribution, 5 Statistical Inference for a Proportion, 6.

Data can either be numerical or categorical, and both nominal and ordinal data are classified as categorical. Categorical data can be counted, grouped and sometimes ranked in order of importance. Numerical data can be measured. With categorical data, events or information can be placed into groups to bring some sense of order or understanding.

Categorical Data Analysis. Categorical data is data that classifies an observation as belonging to one or more categories. For example, an item might be judged as good or bad, or a response to a survey might includes categories such as agree, disagree, or no opinion.

ALAN AGRESTI is Distinguished Professor Emeritus in the Department of Statistics at the University of has presented short courses on categorical data methods in thirty countries. He is the author of five other books, including An Introduction to Categorical Data Analysis, Second Edition and Analysis of Ordinal Categorical Data, Second Edition, both 5/5(1).

Principal Component Analysis is really, really useful. You use it to create a single index variable from a set of correlated variables. In fact, the very first step in Principal Component Analysis is to create a correlation matrix (a.k.a., a table of bivariate correlations).

The rest of the analysis is based on this correlation matrix. You don't usually see this step -- it happens behind the.

A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software.

Readers will find a unified generalized linear models. "A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis." – Statistics in Medicine on Categorical Data Analysis, First Edition The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences.

Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses.

Readers will find a unified generalized linear models approach that. If you’re interested in learning more about categorical data analysis, a good first choice would be Agresti which, as the title suggests, provides an Introduction to Categorical Data Analysis.

If the introductory book isn’t enough for you (or can’t solve the problem you’re working on) you could consider Agresti (), Categorical. 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. such as presented in Chapters 1 to 6 of my books An Introduction to Categorical Data Analysis (2nd ed., Wiley, ) and Categorical Data Analysis (2nd ed., Wiley, ).

On an ordinal scale, the technical level of this book is intended to fall between that of the two books just mentioned. I intend the book to be accessible to a broad. Frequently, categorical data are presented in tabular form, known as contingency tables. Categorical data analysis is concerned with the analysis of categorical response measures, regardless of whether any accompanying explanatory variables are also categorical or are continuous.

This book discusses hypothesis testing strategies. Treating ordinal variables as numeric. That downside is a big one.

Because they’re worried about losing the information in the ordering, many data analysts go to the other end: ignore the fact that the ordinal variable really isn’t numeric and treat the numerals that designate each category as actual numbers.

I need to run exploratory factor analysis for some categorical variables (on 0,1,2 likert scale). In the Factor procedure dialogs (Analyze->Dimension Reduction->Factor), I do not see an option for defining the variables as categorical.

Do I need to set the Measure for each variable to 'Ordinal' in the Variable View of the Data Editor. cedures discussed here can be used on any type of categorical data. There are some specific procedures for ordinal data, and they will be briefly discussed later in the chapter.

Statisticians have devised a number of ways to analyze and explain categorical data. This chapter presents explanations of each of the following methods:File Size: 1MB. 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 Brand: Wiley. Categorical Data Analysis With SAS(R) and SPSS Applications features: *detailed programs and outputs of all examples illustrated in the book using SAS(R) and SPSS on the book's CD; *detailed coverage of topics often ignored in other books, such as one-way classification (ch.

3), the analysis of doubly classified data (ch. 11), and.A model very close to binary logit (for binary data) or proportional log odds (for ordinal data) model is applied.

The algorithm is not tied with decomposing of a correlation matrix, so it is a bit away from traditional FA, still it is a bona fide categorical FA.2 Ordinal categorical responses • Patient quality of life (excellent, good, fair, poor) • Political philosophy (very liberal, slightly liberal, moderate, slightly conservative, very conservative) • Government spending (too low, about right, too high) • Categorization of an inherently continuous variable, such as body mass index, BMI = weight(kg)/[height(m)]2,File Size: KB.

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