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Learning more from social experiments : evolving analytic approaches

Author: Howard S Bloom
Publisher: New York : Russell Sage Foundation, ©2005.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
"Using evaluations of actual social programs as examples, Learning More from Social Experiments makes the case that many of the limitations of random-assignment studies can be overcome by combining data from these studies with statistical methods from other research designs." "Bloom evaluates the properties of cluster randomization, its relevance to different kinds of social programs, and the complications that  Read more...
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Document Type: Book
All Authors / Contributors: Howard S Bloom
ISBN: 0871541270 9780871541277
OCLC Number: 57068882
Notes: "An MDRC project."
Description: xi, 246 pages : illustrations ; 24 cm
Contents: Precedents and prospects for randomized experiments / Charles Michalopoulos --
Modeling cross-site experimental differences to find out why program effectiveness varies / Howard S. Bloom, Carolyn J. Hill, and James A. Riccio --
Constructing instrumental variables from experimental data to explore how treatments produce effects / Lisa A. Gennetian [and others] --
Randomized groups to evaluate place-based programs / Howard S. Bloom --
Using experiments to assess nonexperimental comparison-group methods for measuring program effects / Howard S. Bloom, Charles Michalopoulos, and Carolyn J. Hill.
Responsibility: Howard S. Bloom, editor.

Abstract:

"Using evaluations of actual social programs as examples, Learning More from Social Experiments makes the case that many of the limitations of random-assignment studies can be overcome by combining data from these studies with statistical methods from other research designs." "Bloom evaluates the properties of cluster randomization, its relevance to different kinds of social programs, and the complications that arise from its use. He pays particular attention to the way in which the movement of individuals into and out of clusters over time complicates the design, execution, and interpretation of a study."--Jacket.

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