Автор: Liu, Xing Название: Categorical data analysis and multilevel modeling using r ISBN: 1544324901 ISBN-13(EAN): 9781544324906 Издательство: Sage Publications Рейтинг: Цена: 120390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Categorical Data Analysis and Multilevel Modeling Using Rprovides 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.
Автор: Tian, Guo-Liang , Tang, Man-Lai Название: Incomplete Categorical Data Design ISBN: 0367379627 ISBN-13(EAN): 9780367379629 Издательство: Taylor&Francis Рейтинг: Цена: 65320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Respondents to survey questions involving sensitive information, such as sexual behavior, illegal drug usage, tax evasion, and income, may refuse to answer the questions or provide untruthful answers to protect their privacy. This creates a challenge in drawing valid inferences from potentially inaccurate data. Addressing this difficulty, non-randomized response approaches enable sample survey practitioners and applied statisticians to protect the privacy of respondents and properly analyze the gathered data.
Incomplete Categorical Data Design: Non-Randomized Response Techniques for Sensitive Questions in Surveys is the first book on non-randomized response designs and statistical analysis methods. The techniques covered integrate the strengths of existing approaches, including randomized response models, incomplete categorical data design, the EM algorithm, the bootstrap method, and the data augmentation algorithm.
A self-contained, systematic introduction, the book shows you how to draw valid statistical inferences from survey data with sensitive characteristics. It guides you in applying the non-randomized response approach in surveys and new non-randomized response designs. All R codes for the examples are available at www.saasweb.hku.hk/staff/gltian/.
Автор: Azen, Razia (the University Of Wisconsin- Milwaukee, Usa) Walker, Cindy M. (the University Of Wisconsin- Milwaukee, Usa) Название: Categorical data analysis for the behavioral and social sciences ISBN: 0367352761 ISBN-13(EAN): 9780367352769 Издательство: Taylor&Francis Рейтинг: Цена: 93910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market. The authors cover common categorical analysis methods and emphasize specific research questions that can be addressed by each analytic procedure, including how to obtain results using SPSS, SAS, and R, so that readers are able to address the research questions they wish to answer.Each chapter begins with a "Look Ahead" section to highlight key content. This is followed by an in-depth focus and explanation of the relationship between the initial research question, the use of software to perform the analyses, and how to interpret the output substantively. Included at the end of each chapter are a range of software examples and questions to test knowledge.New to the second edition:The addition of R syntax for all analyses and an update of SPSS and SAS syntax.The addition of a new chapter on GLMMs.Clarification of concepts and ideas that graduate students found confusing, including revised problems at the end of the chapters.Written for those without an extensive mathematical background, this book is ideal for a graduate course in categorical data analysis taught in departments of psychology, educational psychology, human development and family studies, sociology, public health, and business. Researchers in these disciplines interested in applying these procedures will also appreciate this book’s accessible approach.
Автор: Azen Razia, Walker Cindy M. Название: Categorical Data Analysis for the Behavioral and Social Sciences ISBN: 0367352745 ISBN-13(EAN): 9780367352745 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Featuring a practical approach with numerous examples, the second edition of Categorical Data Analysis for the Behavioral and Social Sciences focuses on helping the reader develop a conceptual understanding of categorical methods, making it a much more accessible text than others on the market.
Автор: Heck, Ronald H Название: Multilevel Modeling of Categorical Outcomes Using IBM SPSS ISBN: 1848729561 ISBN-13(EAN): 9781848729568 Издательство: Taylor&Francis Рейтинг: Цена: 51030.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Parimal Mukhopadhyay Название: Complex Surveys ISBN: 9811008701 ISBN-13(EAN): 9789811008702 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The primary objective of this book is to study some of the research topics in the area of analysis of complex surveys which have not been covered in any book yet. Many large-scale sample surveys collect data using complex survey designs like multistage stratified cluster designs.
Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher. Mathematical derivations are placed in optional appendices for those interested in this detailed coverage.
Highlights include the following:
Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project; however, some chapters can be omitted entirely if preferred.
Step-by-step examples of each test help readers see how the material is applied in a variety of disciplines.
Although the book can be used with any program, examples of how to use the tests in SPSS and Excel foster conceptual understanding.
Exploring the Concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding.
Problems in each chapter help readers test their understanding of the material.
Emphasis on selecting tests that maximize power helps readers avoid "marginally" significant results.
Website (www.routledge.com/9781138787827) features datasets for the book's examples and problems, and for the instructor, PowerPoint slides, sample syllabi, answers to the even-numbered problems, and Excel data sets for lecture purposes.
Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t tests and ANOVA.
Автор: Martin, Peter Название: Regression models for categorical and count data ISBN: 1529761263 ISBN-13(EAN): 9781529761269 Издательство: Sage Publications Рейтинг: Цена: 28500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this engaging and well-illustrated volume of the SAGE Quantitative Research Kit, Peter Martin provides practical guidance on conducting regression analysis on categorical and count data. The author covers both the theory and application of statistical models, with the help of illuminating graphs.
Автор: Ronald H Heck, Scott Thomas, Lynn Tabata Название: Multilevel Modeling of Categorical Outcomes Using IBM SPSS ISBN: 1848729553 ISBN-13(EAN): 9781848729551 Издательство: Taylor&Francis Рейтинг: Цена: 148010.00 T Наличие на складе: Невозможна поставка. Описание: This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors’ highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.
Автор: Nussbaum E Michael Название: Categorical and Nonparametric Data Analysis ISBN: 1138787825 ISBN-13(EAN): 9781138787827 Издательство: Taylor&Francis Рейтинг: Цена: 81650.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher. Mathematical derivations are placed in optional appendices for those interested in this detailed coverage. Highlights include the following: Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project; however, some chapters can be omitted entirely if preferred. Step-by-step examples of each test help readers see how the material is applied in a variety of disciplines. Although the book can be used with any program, examples of how to use the tests in SPSS and Excel foster conceptual understanding. Exploring the Concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding. Problems in each chapter help readers test their understanding of the material. Emphasis on selecting tests that maximize power helps readers avoid "marginally" significant results. Website (www.routledge.com/9781138787827) features datasets for the book's examples and problems, and for the instructor, PowerPoint slides, sample syllabi, answers to the even-numbered problems, and Excel data sets for lecture purposes. Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t tests and ANOVA.
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