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ERIC Number: EJ1089972
Record Type: Journal
Publication Date: 2016
Pages: 25
Abstractor: As Provided
Reference Count: 46
ISBN: N/A
ISSN: ISSN-1934-5747
Assessing Methods for Generalizing Experimental Impact Estimates to Target Populations
Kern, Holger L.; Stuart, Elizabeth A.; Hill, Jennifer; Green, Donald P.
Journal of Research on Educational Effectiveness, v9 n1 p103-127 2016
Randomized experiments are considered the gold standard for causal inference because they can provide unbiased estimates of treatment effects for the experimental participants. However, researchers and policymakers are often interested in using a specific experiment to inform decisions about other target populations. In education research, increasing attention is being paid to the potential lack of generalizability of randomized experiments because the experimental participants may be unrepresentative of the target population of interest. This article examines whether generalization may be assisted by statistical methods that adjust for observed differences between the experimental participants and members of a target population. The methods examined include approaches that reweight the experimental data so that participants more closely resemble the target population and methods that utilize models of the outcome. Two simulation studies and one empirical analysis investigate and compare the methods' performance. One simulation uses purely simulated data while the other utilizes data from an evaluation of a school-based dropout prevention program. Our simulations suggest that machine learning methods outperform regression-based methods when the required structural (ignorability) assumptions are satisfied. When these assumptions are violated, all of the methods examined perform poorly. Our empirical analysis uses data from a multisite experiment to assess how well results from a given site predict impacts in other sites. Using a variety of extrapolation methods, predicted effects for each site are compared to actual benchmarks. Flexible modeling approaches perform best, although linear regression is not far behind. Taken together, these results suggest that flexible modeling techniques can aid generalization while underscoring the fact that even state-of-the-art statistical techniques still rely on strong assumptions.
Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Institute of Mental Health (DHHS/NIH); National Science Foundation (NSF); Institute of Education Sciences (ED)
Authoring Institution: N/A
IES Funded: Yes
Grant or Contract Numbers: K25 MH083846; DRL-1335843; R305D110037