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This book is a step-by-step data story for analysing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with data sets where the response is ordinal. This type of data is common in many disciplines, not just in surveys.
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Nairanjana (aka “Jan”) Dasgupta is a Regents Professor and Boeing Distinguished Professor of Math and Science Education at Washington State University. She also serves as the Professor of Statistics and Director of the Data Analytics Program. She is a Fellow of ASA and an inductee in the WA State Academy of Sciences. She is passionate about the understandability and applicability of statistical methods in real-life applications.
Jillian Morrison is Assistant Professor of Statistical and Data Sciences at The College of Wooster. She serves as Chair of the Communications Committee and a member of the governing council for the Caucus for Women in Statistics and Data Science. She is also a fellow of the Mathematical Association of America’s Project NExT. Her work focuses on applying statistical and data science methods to solve problems in the real world.
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Ordinal Data Analysis
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ORDINAL DATA
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Ordinal Data Analysis
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Ordinal Data Analysis
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Taylor & Francis Ltd Jun 2024, 2024
ISBN 10: 0367855909
ISBN 13: 9780367855901
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Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book is a step-by-step data story for analyzing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with datasets where the response is ordinal. This type of data is common in many disciplines, not just in surveys (as is often thought). For example, in the biological sciences, there is an interest in understanding and predicting the (growth) stage (of a plant or animal) based on a multitude of factors. Likewise, ordinal data is common in environmental sciences (for example, stage of a storm), chemical sciences (for example, type of reaction), physical sciences (for example, stage of damage when force is applied), medical sciences (for example, degree of pain) and social sciences (for example, demographic factors like social status categorized in brackets). There has been no complete text about how to model an ordinal response as a function of multiple numerical and categorical predictors. There has always been a reluctance and reticence towards ordinal data as it lies in a no-man's land between numerical and categorical data. Examples from health sciences are used to illustrate in detail the process of how to analyze ordinal data, from exploratory analysis to modeling, to inference and diagnostics. This book also shows how Likert-type analysis is often used incorrectly and discusses the reason behind it. Similarly, it discusses the methods related to Structural Equations and talks about appropriate uses of this class of methods.The text is meant to serve as a reference book and to be a 'how-to' resource along with the 'why' and 'when' for modeling ordinal data.Key Features:Includes applications of the statistical theoryIncludes illustrated examples with the associated R and SAS codeDiscusses the key differences between the different methods that are used for ordinal data analysisBridges the gap between methods for ordinal data analysis used in different disciplines 190 pp. Englisch. Bestandsnummer des Verkäufers 9780367855901
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Ordinal Data Analysis
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Ordinal Data Analysis: Statistical Perspective with Applications
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Ordinal Data
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Ordinal Data: Statistical Perspective with Applications
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Chapman and Hall/CRC 2024-06-04, 2024
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ISBN 13: 9780367855901
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Ordinal Data Analysis : Statistical Perspective with Applications
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Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book is a step-by-step data story for analyzing ordinal data from start to finish. The book is for researchers, statisticians and scientists who are working with datasets where the response is ordinal. This type of data is common in many disciplines, not just in surveys (as is often thought). For example, in the biological sciences, there is an interest in understanding and predicting the (growth) stage (of a plant or animal) based on a multitude of factors. Likewise, ordinal data is common in environmental sciences (for example, stage of a storm), chemical sciences (for example, type of reaction), physical sciences (for example, stage of damage when force is applied), medical sciences (for example, degree of pain) and social sciences (for example, demographic factors like social status categorized in brackets). There has been no complete text about how to model an ordinal response as a function of multiple numerical and categorical predictors. There has always been a reluctance and reticence towards ordinal data as it lies in a no-man's land between numerical and categorical data. Examples from health sciences are used to illustrate in detail the process of how to analyze ordinal data, from exploratory analysis to modeling, to inference and diagnostics. This book also shows how Likert-type analysis is often used incorrectly and discusses the reason behind it. Similarly, it discusses the methods related to Structural Equations and talks about appropriate uses of this class of methods.The text is meant to serve as a reference book and to be a 'how-to' resource along with the 'why' and 'when' for modeling ordinal data.Key Features:Includes applications of the statistical theoryIncludes illustrated examples with the associated R and SAS codeDiscusses the key differences between the different methods that are used for ordinal data analysisBridges the gap between methods for ordinal data analysis used in different disciplines. Bestandsnummer des Verkäufers 9780367855901
Anzahl: 2 verfügbar