This paper reviews the history of the regression discontinuity design in three academic disciplines. It describes the design's birth and subsequent demise in Psychology even though most problems with it had been solved there. It further describes the scant interest shown in the design by scholars formally trained in Statistics, and the design's poor reception in Economics from 1972 until about 1995, when its profile and acceptance changed. Reasons are given for this checkered history that is characterized as waiting for life to arrive.A version of the paper is freely available here; and tells us that "since its invention in 1960, RDD has been, in Samuel Beckett’s words: waiting for life to happen."
Showing posts with label regression discontinuity. Show all posts
Showing posts with label regression discontinuity. Show all posts
Saturday, February 12, 2011
Samuel Beckett and Regression Discontinuity Design
Posted by
Anonymous
In 2008, Thomas Cook published Waiting for Life to Arrive: A history of the regression-discontinuity design in Psychology, Statistics and Economics, in the Journal of Econometrics. The abstract is as follows:
Wednesday, January 12, 2011
Does Drinking in College Affect Students' Grades?
Posted by
Anonymous
I recently discussed on the blog new evidence pointing towards a positive relationship between students' grades in college and their later-life health outcomes. A recent economics paper adds a new dimension in this area by producing findings on the effect of preceding health-risk behaviour on subsequent academic performance. The paper is by Scott E. Carrell, Mark Hoekstra, and James E. West; and is called "Does Drinking Impair College Performance? Evidence from a Regression Discontinuity Approach". Abstract: here. Paper: here. Discussion on the Freakonomics Blog: here.
The paper exploits a discontinuity in drinking at age 21 at a college in which the minimum legal drinking age is strictly enforced. A comparison is made between the grades of students who turned 21 before final exams to those who turned 21 just afterward. The authors find that drinking causes causes statistically and economically meaningful reductions in academic performance, particularly for the highest-performing students. The authors suggest that concern regarding the harmful effects of drinking in U.S. colleges is reflected by the Amethyst Initiative - in which 135 university presidents and chancellors argue that current policy has resulted in binge-drinking by students.
The paper exploits a discontinuity in drinking at age 21 at a college in which the minimum legal drinking age is strictly enforced. A comparison is made between the grades of students who turned 21 before final exams to those who turned 21 just afterward. The authors find that drinking causes causes statistically and economically meaningful reductions in academic performance, particularly for the highest-performing students. The authors suggest that concern regarding the harmful effects of drinking in U.S. colleges is reflected by the Amethyst Initiative - in which 135 university presidents and chancellors argue that current policy has resulted in binge-drinking by students.
Wednesday, December 10, 2008
Opiates for the Matches
Posted by
Anonymous
A working paper (2008) by Jasjeet S. Sekhon from the Department of Political Science at UC Berkeley: "Opiates for the Matches - Matching Methods for Causal Inference".
Abstract
In recent years there has been a burst of innovative work on methods for estimating causal effects using observational data. Much of this work has extended and brought a renewed focus on old approaches such as matching, which is the focus of this review. The new developments highlight an old tension in the social sciences between a focus on research design versus a focus on quantitative models. This realization along with the renewed interest in field experiments has marked the return of foundational questions as opposed to a fascination with the latest estimator. I use studies of get-out-the-vote interventions to exemplify this development. Without an experiment, natural experiment, a discontinuity, or some other strong design, no amount of econometric or statistical modeling can make the move from correlation to causation persuasive.
Subscribe to:
Posts (Atom)