Cross-level Inference

Achen, Christopher H.; Shively, W.Philips

ISBN 10: 0226002209 / ISBN 13: 9780226002200

Edité par The University of Chicago Press, 1995

A propos de cet article

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. . . . .

N° de réf. du vendeur V9780226002200

Détails bibliographiques

• Titre: Cross-level Inference
• Éditeur: The University of Chicago Press
• Date de parution: 1995
• Langue: anglais
• ISBN à 10 chiffres: 0226002209
• ISBN à 13 chiffres: 9780226002200
• Reliure: Couverture souple
• Édition: Edition originale
• État de l'article: New

A propos de ce titre

Synopsis

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.