Summary
Contents
Subject index
Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it by connecting it to social science research topics. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
Introduction
Introduction
Learning Objectives
- Understand the current status of spatial social science research.
- Understand basic concepts and terminologies related to spatial effects.
- Familiarize yourself with the primary data example of population change that is used throughout this book.
This is a book for social scientists who want to learn spatial regression models with relative ease. But first of all, why should social scientists care about studying spatial regression models? Many statistical methods could be useful to social science research; learning any of them could take one considerable effort and also sometimes involve a steep learning curve.
We believe that quantitative social scientists, especially those who deal with aggregated quantitative data, can benefit from learning and using spatial regression models from at least three perspectives—theoretical, methodological, and practical. First, theoretically, many ...
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