Explains why methods such as ecological regression so often fail and examines the promising new techniques for cross-level inference. The authors argue for a range of alternate techniques, including extensions of cross-tabular, regression analysis and unobservable variable estimators. Num Pages: 256 pages, 8 figures, 9 halftones. BIC Classification: JHBC. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 230 x 152 x 15. Weight in Grams: 360. . 1995. 1st Edition. Paperback. . . . .

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Bibliographic Details

Title: Cross-level Inference
Publisher: The University of Chicago Press
Publication Date: 1995
Language: English
ISBN 10: 0226002209
ISBN 13: 9780226002200
Binding: Soft cover
Edition: 1st Edition
Condition: New

About this title

Synopsis

In the last few years, new disputes have erupted over the use of group averages from census areas or voting districts to draw inferences about individual social behaviour. 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 examines the promising new techniques for cross-level inference. Experts in statistical analysis of aggregate data, Christopher H. Achen and W. Phillips 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 extensions of cross-tabular, regression analysis and unobservable variable estimators.

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An indispensable guide and reference to a critical issue for quantitative researchers in political science and policy analysis, history, sociology, and geography.

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