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Categorical Data Analysis
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 do any analysis of categorical data, this is an essential desktop reference." —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries.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 connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data.This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects modelsTwo new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysisNew sections introducing the Bayesian approach for methods in that chapterMore than 100 analyses of data sets and over 600 exercisesNotes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sourcesA supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.
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Applied Categorical and Count Data Analysis
Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis, Second Edition explains how to perform the statistical analysis of discrete data, including categorical and count outcomes.The authors have been teaching categorical data analysis courses at the University of Rochester and Tulane University for more than a decade.This book embodies their decade-long experience and insight in teaching and applying statistical models for categorical and count data.The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without relying on rigorous mathematical arguments. The second edition is a major revision of the first, adding much new material.It covers classic concepts and popular topics, such as contingency tables, logistic regression models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values.As in the first edition, R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers.It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields. Features:Describes the basic ideas underlying each concept and modelIncludes R, SAS, SPSS and Stata programming codes for all the examplesFeatures significantly expanded Chapters 4, 5, and 8 (Chapters 4-6, and 9 in the second editionExpands discussion for subtle issues in longitudinal and clustered data analysis such as time varying covariates and comparison of generalized linear mixed-effect models with GEE
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Analysis of Categorical Data with R
Analysis of Categorical Data with R, Second Edition presents a modern account of categorical data analysis using the R software environment.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.The authors give detailed advice and guidelines on which procedures to use and why to use them. The second edition is a substantial update of the first based on the authors’ experiences of teaching from the book for nearly a decade.The book is organized as before, but with new content throughout, and there are two new substantive topics in the advanced topics chapter—group testing and splines.The computing has been completely updated, with the "emmeans" package now integrated into the book.The examples have also been updated, notably to include new examples based on COVID-19, and there are more than 90 new exercises in the book.The solutions manual and teaching videos have also been updated. Features:Requires no prior experience with R, and offers an introduction to the essential features and functions of RIncludes numerous examples from medicine, psychology, sports, ecology, and many other areasIntegrates extensive R code and outputGraphically demonstrates many of the features and properties of various analysis methodsOffers a substantial number of exercises in all chapters, enabling use as a course text or for self-studySupplemented by a website with data sets, code, and teaching videosAnalysis of Categorical Data with R, Second Edition is primarily designed for a course on categorical data analysis taught at the advanced undergraduate or graduate level.Such a course could be taught in a statistics or biostatistics department, or within mathematics, psychology, social science, ecology, or another quantitative discipline.It could also be used by a self-learner and would make an ideal reference for a researcher from any discipline where categorical data arise.
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An Introduction to Categorical Data Analysis
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 approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
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Is the categorical imperative applicable to every culture?
The categorical imperative, as formulated by Immanuel Kant, is a moral principle that states that one should act according to a maxim that can be willed to be a universal law. While this principle may have universal applicability in terms of its logical consistency, its practical application may vary across different cultures. Different cultures may have different moral values and norms, which may not always align with the categorical imperative. Therefore, while the principle itself may be universal, its application may need to be adapted to the specific cultural context.
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Are the categorical imperative and the law of nature synonymous?
No, the categorical imperative and the law of nature are not synonymous. The categorical imperative, proposed by Immanuel Kant, is a moral principle that states one should act only according to rules that they would be willing to see universally followed. On the other hand, the law of nature refers to the idea that there are certain inherent laws or principles that govern the natural world. While both concepts involve principles that guide behavior, they are distinct in their focus and application.
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What is Kant's categorical imperative?
Kant's categorical imperative is a moral principle that states that one should act only according to rules that they would be willing to see universally followed. In other words, it is a command that applies to all rational beings, regardless of their personal desires or circumstances. Kant believed that this principle provides a basis for determining what is morally right or wrong, as it focuses on the intention behind an action rather than the consequences. Ultimately, the categorical imperative serves as a guide for individuals to act in a way that respects the inherent dignity and autonomy of all individuals.
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Is the categorical imperative rigorous?
Yes, the categorical imperative is considered to be rigorous because it provides a clear and universal moral principle that applies to all rational beings. It demands that individuals act according to principles that could be willed as universal laws, and it requires individuals to treat others as ends in themselves, rather than as means to an end. This rigorous framework helps to guide moral decision-making and promote ethical behavior.
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Regression Models for Categorical and Count Data
This text provides practical guidance on conducting regression analysis on categorical and count data.Step by step and supported by lots of helpful graphs, it covers both the theoretical underpinnings of these methods as well as their application, giving you the skills needed to apply them to your own research.It offers guidance on: · Using logistic regression models for binary, ordinal, and multinomial outcomes · Applying count regression, including Poisson, negative binomial, and zero-inflated models · Choosing the most appropriate model to use for your research · The general principles of good statistical modelling in practice Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey
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Categorical Data Analysis and Multilevel Modeling Using R
Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software.Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches.Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication.A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.
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Applications of Regression for Categorical Outcomes Using R
This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial).For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided.There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis.Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences.Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide.Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator.Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now.Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable.We will leverage this feature to yield high-end graphical displays of results Affordability- R is free.R packages are free. There is no need to purchase site licenses or updates.
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Applications of Regression for Categorical Outcomes Using R
This book covers the main models within the GLM (i.e., logistic, Poisson, negative binomial, ordinal, and multinomial).For each model, estimations, interpretations, model fit, diagnostics, and how to convey results graphically are provided.There is a focus on graphic displays of results as these are a core strength of using R for statistical analysis.Many in the social sciences are transitioning away from using Stata, SPSS and SAS, to using R, and this book uses statistical models which are relevant to the social sciences.Social Science Applications of Regression for Categorical Outcomes Using R will be useful for graduate students in the social sciences who are looking to expand their statistical knowledge, and for Quantitative social scientists due to it’s ability to act as a practitioners guide.Key Features: Applied- in the sense that we will provide code that others can easily adapt Flexible- R is basically just a fancy calculator.Our programs will enable users to derive quantities that they can use in their work Timely- many in the social sciences are currently transitioning to R or are learning it now.Our book will be a useful resource Versatile- we will write functions into an R package that can be applied to all of the regression models we will cover in the book Aesthetically pleasing- one advantage of R relative to other software packages is that graphs are fully customizable.We will leverage this feature to yield high-end graphical displays of results Affordability- R is free.R packages are free. There is no need to purchase site licenses or updates.
Price: 61.99 £ | Shipping*: 0.00 £
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What exactly is a categorical imperative?
A categorical imperative is a moral principle that is unconditional and applies to all rational beings, regardless of their desires or circumstances. It is a command that is derived from reason and applies to all moral agents universally. In other words, it is a rule that one must follow simply because it is the right thing to do, without any consideration of personal gain or consequences. This concept was developed by the philosopher Immanuel Kant as a way to establish a foundation for moral duties and obligations.
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What is Immanuel Kant's categorical imperative?
Immanuel Kant's categorical imperative is a moral principle that states that one should act in a way that they would want everyone else to act in similar circumstances. It emphasizes the importance of universalizing one's actions and treating others as ends in themselves, rather than as a means to an end. Kant believed that this principle provides a basis for determining what is morally right or wrong, and that it is a fundamental aspect of moral reasoning.
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Is the categorical imperative still relevant today?
Yes, the categorical imperative, as proposed by Immanuel Kant, is still relevant today. Its fundamental principle of treating others as ends in themselves rather than as means to an end is a timeless ethical concept that can guide moral decision-making in various contexts. In a world where ethical dilemmas and moral ambiguity are prevalent, the categorical imperative provides a clear and universal framework for determining right from wrong. Its emphasis on rationality and consistency in moral reasoning remains valuable in contemporary society.
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What is the categorical imperative regarding suicide?
The categorical imperative regarding suicide is that one should never treat themselves merely as a means to an end, but always as an end in themselves. This means that individuals should not take their own lives as a means to escape suffering or difficulties, but should instead consider their intrinsic value and worth as a human being. Suicide is seen as a violation of this principle because it involves treating oneself as a mere means to end one's suffering, rather than recognizing one's inherent dignity and worth.
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