Logistic Regression Models For Ordinal Response Variables
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Author | : Ann A. O'Connell |
Publisher | : SAGE |
Total Pages | : 124 |
Release | : 2006 |
Genre | : Mathematics |
ISBN | : 9780761929895 |
Ordinal measures provide a simple and convenient way to distinguish among possible outcomes. The book provides practical guidance on using ordinal outcome models.
Author | : Ann A. O′Connell |
Publisher | : SAGE Publications |
Total Pages | : 121 |
Release | : 2005-11-02 |
Genre | : Social Science |
ISBN | : 1452210837 |
Logistic Regression Models for Ordinal Response Variables provides applied researchers in the social, educational, and behavioral sciences with an accessible and comprehensive coverage of analyses for ordinal outcomes. The content builds on a review of logistic regression, and extends to details of the cumulative (proportional) odds, continuation ratio, and adjacent category models for ordinal data. Description and examples of partial proportional odds models are also provided. This book is highly readable, with lots of examples and in-depth explanations and interpretations of model characteristics. SPSS and SAS are used for all examples; data and syntax are available from the author′s website. The examples are drawn from an educational context, but applications to other fields of inquiry are noted, such as HIV prevention, behavior change, counseling psychology, social psychology, etc.). The level of the book is set for applied researchers who need to quickly understand the use and application of these kinds of ordinal regression models.
Author | : Keith McNulty |
Publisher | : CRC Press |
Total Pages | : 272 |
Release | : 2021-07-29 |
Genre | : Business & Economics |
ISBN | : 1000427897 |
Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) Clear step-by-step instructions on executing the analyses Clear guidance on how to interpret results Primary instruction in R but added sections for Python coders Discussion exercises and data exercises for each of the main chapters Final chapter of practice material and datasets ideal for class homework or project work.
Author | : Alan Agresti |
Publisher | : John Wiley & Sons |
Total Pages | : 376 |
Release | : 2012-07-06 |
Genre | : Mathematics |
ISBN | : 1118209990 |
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.
Author | : Xing Liu |
Publisher | : SAGE Publications |
Total Pages | : 372 |
Release | : 2015-09-30 |
Genre | : Social Science |
ISBN | : 1483319768 |
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software. An open-access website for the book contains data sets, Stata code, and answers to in-text questions.
Author | : Scott W. Menard |
Publisher | : SAGE |
Total Pages | : 393 |
Release | : 2010 |
Genre | : Mathematics |
ISBN | : 1412974836 |
Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.
Author | : Jason W. Osborne |
Publisher | : SAGE Publications |
Total Pages | : 489 |
Release | : 2016-03-24 |
Genre | : Psychology |
ISBN | : 1506302750 |
In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
Author | : Jason W. Osborne |
Publisher | : SAGE Publications |
Total Pages | : 489 |
Release | : 2014-02-26 |
Genre | : Social Science |
ISBN | : 1483312097 |
Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.
Author | : Vani K. Borooah |
Publisher | : SAGE |
Total Pages | : 108 |
Release | : 2002 |
Genre | : Mathematics |
ISBN | : 9780761922421 |
Many problems in the social sciences are amenable to analysis using the analytical tools of logit and probit models. This book explains what ordered and multinomial models are and also shows how to apply them to analysing issues in the social sciences.
Author | : Clifford C. Clogg |
Publisher | : SAGE Publications, Incorporated |
Total Pages | : 206 |
Release | : 1994-02-28 |
Genre | : Mathematics |
ISBN | : |
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.