In the last several years, new disputes have erupted over the use of group averages from census areas or voting districts to draw inferences about individual social behavior. Social scientists, policy analysts, and historians often have little choice about using this kind of data, but statistical analysis of them is fraught with pitfalls. The recent debates have led to a new menu of choices for the applied researcher. This volume explains why older methods like ecological regression so often fail, and it gives the most comprehensive treatment available of the promising new techniques for cross-level inference.
Experts in statistical analysis of aggregate data, Christopher H. Achen and W. Philips Shively contend that cross-level inference makes unusually strong demands on substantive knowledge, so that no one method, such as Goodman's ecological regression, will fit all situations. Criticizing Goodman's model and some recent attempts to replace it, the authors argue for a range of alternate techniques, including estensions of cross-tabular, regression analysis, and unobservable variable estimators.
Table of Contents
1: Cross-Level Inference
2: Ecological Regression and Its Extensions
3: Bias in Goodman Ecological Regression: Partisanship Models
4: Problems of Specification in Cross-Level Models
5: Relaxing the Goodman Assumptions: Improved Estimation Using a Subset ofthe Assumptions
6: Relaxing the Goodman Assumptions: Working with Underidentified Models
7: Models with Unobservable Variables: More Partisanship Models
8: Tabular Approaches: The Method of Bounds and the Method of Differences
9: Contextual Studies and Aggregation