Summary
Contents
Subject index
This book introduces researchers and students to the concepts and generalized linear models for analyzing quantitative random variables that have one or more bounds. Examples of bounded variables include the percentage of a population eligible to vote (bounded from 0 to 100), or reaction time in milliseconds (bounded below by 0). The human sciences deal in many variables that are bounded. Ignoring bounds can result in misestimation and improper statistical inference. Michael Smithson and Yiyun Shou’s book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.
Introduction and Overview
Introduction and Overview
Overview of This Book
This book provides a course on generalized linear models for bounded variables. We focus on numeric dependent variables whose scales are bounded either at one end or both ends. Examples are income (typically bounded below at 0), hours spent on an activity per day (bounded between 0 and 24), or percentage of a population eligible to vote (bounded from 0 to 100).
Why is this topic important? The human sciences deal in many variables that are bounded. Ignoring bounds can result in misestimation and improper statistical inference. On the other hand, taking bounds into account not only can provide more accurate statistics but also often reveals insights that otherwise would escape the researcher’s notice.
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