Logistic Regression Models for Ordinal Response Variables

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

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Author: Ann A. O'Connell

Publisher: SAGE

ISBN: 0761929894

Category: Mathematics

Page: 107

View: 759

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

Applied Ordinal Logistic Regression Using Stata

Chapters 4 through 9 present various techniques for analyzing ordinal response variables for single-level data. They start with the most commonly used model (the proportional odds model), then proceed with more ...

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Author: Xing Liu

Publisher: SAGE Publications

ISBN: 9781483319742

Category: Social Science

Page: 552

View: 248

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 by Xing Liu 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.

Handbook of Multilevel Analysis

Multilevel. Models. for. Ordinal. and. Nominal. Variables. Donald Hedeker University of Illinois at Chicago ... For dichotomous response data, several approaches adopting either a logistic or probit regression model and various methods ...

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Author: Jan Deleeuw

Publisher: Springer Science & Business Media

ISBN: 9780387731865

Category: Mathematics

Page: 494

View: 719

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.

Multiple Regression with Discrete Dependent Variables

regressions, and use binary logistic regression methods to detect outliers and influential observations (Hosmer & Lemeshow, ... Additional Regression Models for Ordinal Dependent Variables In addition to ordinal logistic regression, ...

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Author: John G. Orme

Publisher: Oxford University Press

ISBN: 9780195329452

Category: Social Science

Page: 206

View: 606

This volume presents detailed discussions of regression models that are appropriate for a variety of discrete dependent variables. Clear language guides the reader briefly through each step of the analysis, using SPSS and result presentation to enhance understanding of the important link function.

Best Practices in Quantitative Methods

is on extensions of binary logistic regression to models designed for nominal response variables. Furthermore, a solid understanding of the models for nominal responses facilitates mastering models for ordinal data.

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Author: Jason W. Osborne

Publisher: SAGE

ISBN: 9781412940658

Category: Social Science

Page: 596

View: 258

The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

The SAGE Handbook of Regression Analysis and Causal Inference

Journal of Mathematical Sociology, 4, 103–120. O'Connell, A. A. (2006). Logistic Regression Models for Ordinal Response Variables. Thousand Oaks, CA: Sage. Small, K. A. and Hsiao, C. (1985). Multinomial logit specification tests.

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Author: Henning Best

Publisher: SAGE

ISBN: 9781473908352

Category: Reference

Page: 424

View: 145

'The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.' - John Fox, Professor, Department of Sociology, McMaster University 'The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.' - Ben Jann, Executive Director, Institute of Sociology, University of Bern 'Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.' -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.

Logistic Regression Models

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. ... R.W. (2006). gologit2: Generalized ordered logit/partial proportional odds models for ordinal dependent variables. Stata Journal 6:58–82.

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Author: Joseph M. Hilbe

Publisher: CRC Press

ISBN: 9781420075779

Category: Mathematics

Page: 656

View: 448

Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models to health, environmental, physical, and social science data. Examples illustrate successful modeling The text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and IRLS) appropriate for logistic models. It then presents an in-depth discussion of related terminology and examines logistic regression model development and interpretation of the results. After focusing on the construction and interpretation of various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties of overdispersion, and a number of extensions to the basic binary and binomial logistic model. Both real and simulated data are used to explain and test the concepts involved. The appendices give an overview of marginal effects and discrete change as well as a 30-page tutorial on using Stata commands related to the examples used in the text. Stata is used for most examples while R is provided at the end of the chapters to replicate examples in the text. Apply the models to your own data Data files for examples and questions used in the text as well as code for user-authored commands are provided on the book’s website, formatted in Stata, R, Excel, SAS, SPSS, and Limdep. See Professor Hilbe discuss the book.

Predictive Modeling Applications in Actuarial Science

The ROC curve for the logistic regression model in the FullCoverage. csv dataset. Dependent variable is the ... This section describes the cumulative logit regression model aimed at ordinal dependent variables. It focuses on model ...

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Author: Edward W. Frees

Publisher: Cambridge University Press

ISBN: 9781107029873

Category: Business & Economics

Page: 563

View: 737

This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

Predictive Modeling Applications in Actuarial Science Volume 1 Predictive Modeling Techniques

The ROC curve for the logistic regression model in the FullCoverage. csv dataset. Dependent variable is the ... This section describes the cumulative logit regression model aimed at ordinal dependent variables. It focuses on model ...

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Author: Edward W. Frees

Publisher: Cambridge University Press

ISBN: 9781139992312

Category: Business & Economics

Page:

View: 250

Predictive modeling involves the use of data to forecast future events. It relies on capturing relationships between explanatory variables and the predicted variables from past occurrences and exploiting this to predict future outcomes. Forecasting future financial events is a core actuarial skill - actuaries routinely apply predictive-modeling techniques in insurance and other risk-management applications. This book is for actuaries and other financial analysts who are developing their expertise in statistics and wish to become familiar with concrete examples of predictive modeling. The book also addresses the needs of more seasoned practising analysts who would like an overview of advanced statistical topics that are particularly relevant in actuarial practice. Predictive Modeling Applications in Actuarial Science emphasizes lifelong learning by developing tools in an insurance context, providing the relevant actuarial applications, and introducing advanced statistical techniques that can be used by analysts to gain a competitive advantage in situations with complex data.

Statistics in Criminal Justice

... they were left with a choice of fitting the proportional odds model and violating a key assumption of the model or of fitting a multinomial logistic regression model and ignoring the ordinal nature of the dependent variable, ...

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Author: David Weisburd

Publisher: Springer Science & Business Media

ISBN: 9781461491705

Category: Social Science

Page: 783

View: 871

Statistics in Criminal Justice takes an approach that emphasizes the application and interpretation of statistics in research in crime and justice. This text is meant for both students and researchers who want to gain a basic understanding of common statistical methods used in this field. In general, the text relies on a building-block approach, meaning that each chapter helps to prepare the student for the chapters that follow. It also means that the level of sophistication of the text increases as the text progresses. Throughout the text there is an emphasis on comprehension and interpretation, rather than computation. However, as the statistical methods discussed become more complex and demanding to compute, there is increasing use and integration of statistical software. This approach is meant to provide the reader with an accessible, yet sophisticated understanding of statistics that can be used to examine real-life criminal justice problems with popular statistical software programs. The primary goal of the text is to give students and researchers a basic understanding of statistical concepts and methods that will leave them with the confidence and the tools for tackling more complex problems on their own. New to the 4th Edition · New chapter on experimental design and the analysis of experimental data. · New chapter on multi-level models, including growth-curve models. · New computer exercises throughout the text to illustrate the use of both SPSS and Stata. · Revision of exercises at the end of each chapter that places greater emphasis on using statistical software. · Additional resources on the text’s web site for instructors and students, including answers to selected problems, syntax for replicating text examples in SPSS and Stata, and other materials that can be used to supplement the use of the text.