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.
Advanced Models Dealing With Spatial Dependence
Advanced Models Dealing With Spatial Dependence
Learning Objectives
- Understand when and how to fit a spatial error model with spatially lagged responses, interpret the spatial lag effect and the spatial error effect, and use appropriate diagnostics to assess the model.
- Understand when and how to fit a spatial cross-regressive model, interpret the effect of the spatially lagged explanatory variables, and use appropriate diagnostics to assess the model.
- Identify appropriate spatial weight matrices to use in a spatial error model with spatially lagged responses and a spatial cross-regressive model.
- Understand when and how to fit a multilevel linear regression model and interpret the effects at two levels, the reliability estimates, and within- and across-group variances.
- Describe the cautions in fitting a multilevel linear regression model.
In Chapter 3, we introduced spatial ...
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