Ordinal Data Analysis Statistical Perspective With Applications Nairanjana Dasgupta by Nairanjana Dasgupta, Jillian Morrison 9780367855901, 0367855909 instant download after payment.

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 data sets 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. This is true 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) as well. 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 toward 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 theory Includes illustrated examples with the associated R and SAS code Discusses the key differences between the different methods that are used for ordinal data analysis. Bridges the gap between methods for ordinal data analysis used in different disciplines.