Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to sign up for SAGE Journal Email Alerts today!

Sign In to gain access to subscriptions and/or personal tools.
Evaluation Review
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Trochim, W. M.K.
Right arrow Articles by Reichardt, C. S.
Right arrow Search for Related Content
PubMed
Right arrow Articles by Trochim, W. M.K.
Right arrow Articles by Reichardt, C. S.
Social Bookmarking
 Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Random Measurement Error Does Not Bias the Treatment Effect Estimate in the Regression-Discontinuity Design

II. When an Interaction Effect Is Present

William M.K. Trochim

Cornell University

Joseph C. Cappelleri

Cornell University

Charles S. Reichardt

University of Denver

This article examines the regression-discontinuity (RD) design when there is random measure ment error and a treatment interaction effect. Two simulation issues -the specification of the pretest-posttest functional form and the choice of the point-of-estimation of the treatment effect- are examined Traditionally, an interaction effect in the general linear model has been con structed after centering the true scores by subtracting their mean. However, because the RD design has traditionally estimated the treatment effect at the cutoff, one is liable to obtain an apparently biased treatment effect that is actually attributable to the misspecification with regard to the point-of-estimation. Formulas are provided that allow one to control exactly in simulations the magnitude of a treatment effect at any point-of-estimation. These formulas can also be used for simulating the randomized experimental (RE) case where estimation is not at the pretest mean.

Evaluation Review, Vol. 15, No. 5, 571-604 (1991)
DOI: 10.1177/0193841X9101500504


Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
Organizational Research MethodsHome page
S. Mellor and M. M. Mark
A Quasi-Experimental Design for Studies on the Impact of Administrative Decisions: Applications and Extensions of the Regression-Discontinuity Design
Organizational Research Methods, July 1, 1998; 1(3): 315 - 333.
[Abstract]


Home page
Med Decis MakingHome page
J. C. Cappelleri and W. M.K. Trochim
Ethical and Scientific Features of Cutoff-based Designs of Clinical Trials: A Simulation Study
Med Decis Making, October 1, 1995; 15(4): 387 - 394.
[Abstract] [PDF]


Home page
Eval RevHome page
C. S. Reichardt, W. M.K. Trochim, and J. C. Cappelleri
Reports of the Death of Regression-Discontinuity Analysis are Greatly Exaggerated
Eval Rev, February 1, 1995; 19(1): 39 - 63.
[Abstract] [PDF]


Home page
Eval RevHome page
J. C. Cappelleri, R. B. Darlington, and W. M.K. Trochim
Power Analysis of Cutoff-Based Randomized Clinical Trials
Eval Rev, April 1, 1994; 18(2): 141 - 152.
[Abstract] [PDF]