This methodological brief introduces the reader to the regression discontinuity design rdd, which is a method that when used correctly can yield estimates of research treatment effects that are equivalent to those obtained through randomized control trials and can therefore be used to infer causality. I presume that a way to start exploring the method is to enter the treatment variables twice, that is, once with an interaction indicating treatment, and once with an interaction indicating no treatment. Treatment effect estimates from a regression discontinuity design rdd. Regression discontinuity practical applications causal inference there are 5 basic empirical methods to obtain causal inference. The regressiondiscontinuity design rd is a powerful methodological. Regressiondiscontinuity design 1 regressiondiscontinuity design. The regression discontinuity design builds on the preexperimental static group comparison design by introducing a series of comparison groups, addressing the issue of selection as a potential rival explanation. Introduction to regression discontinuity rd designs rd designs as local randomized experiments and the manipulation problem. This includes studying consumer buying habits, responses to treatments or analyzing credit risk. The rdrobust package provides stata and r implementations of statistical inference and graphical procedures for regression discontinuity designs employing local polynomial and partitioning methods. Basic idea is that the assignment to the treatment is going to be. There has been a growing use of regression discontinuity design rdd, introduced by thistlewaite and campbell 1960, in evaluating impacts of development programs. In statistics, econometrics, political science, epidemiology, and related disciplines, a regression discontinuity design rdd is a quasiexperimental pretestposttest design that elicits the causal effects of interventions by assigning a cutoff or threshold above or below which an intervention is assigned.
By comparing observations lying closely on either side of the threshold, it is possible. How the regressiondiscontinuity design got its name. We discuss similarities and differences between these packages and provide directions on how to use them effectively. In the popular regressiondiscontinuity rd design, the use of graphical analysis has been strongly advocated because it provides both easy presentation and transparent validation of the design.
Technical working papers in regression discontinuity rd designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a. This methodological brief introduces the reader to the regression discontinuity design rdd, which is a method that when used correctly can yield estimates of. How can i implement this analysis with the regression module in spss statistics. Properly estimating the treatment effect requires twostage least squares regression. Evidence from a regression discontinuity approach by raffaello bronzini and eleonora iachini.
An intuitive introduction to regression discontinuity. This document contains revised regression discontinuity design rdd standards that have been developed by the wwc in consultation with experts and will be implemented once they become part of a new wwc procedures and standards handbook, planned for 2017 the wwc procedures and standards handbook provides a detailed description of the standards and procedures of the wwc. Owen ozier the world bank regression discontinuity 6 may 2015 2 29 regression discontinuity basic idea a precise rule based on a continuous characteristic determines participation in a program. Yeaton 2006 used the regression discontinuity design to explore the effectiveness of a.
Rdd is one of the most powerful tools in the suite of quasiexperimental methods used to. Lee and thomas lemieux this paper provides an introduction and user guide to regression discontinuity rd designs for empirical researchers. Counterfactual outcomes at the cutpoint of a regression discontinuity. It presents the basic theory behind the research design, details when rd is likely to be valid or invalid given economic incentives. Regression discontinuity for causal effect estimation in. The probability of receiving treatment as a function of the rating. For example, if the assignment variable z follows a uniform distribution over the range 0, 10, patients who are randomly assigned a value of z of. This article describes the analysis of regressiondiscontinuity designs rdds using the r packages rdd, rdrobust, and rddtools. R extension commands for spss statistics ibm knowledge center. Kerry tanner, in research methods second edition, 2018. House of representatives from 1942 to 2008 collected by caughey and sekhon 2011 and a regression discontinuity design examining incumbency advantage to illustrate how a researcher can use plots to present rd results in. The dialogs were created with the custom dialog builder in ibm spss statistics.
Dealing with bias in treatment effects estimated from nonexperimental data. You can view the design for any of the dialogs and you can customize them using the custom dialog builder. Regression discontinuity design rdd is an impact evaluation method that can be used for programs that have a continuous eligibility index with a clearly defined eligibility threshold cutoff score to determine who is eligible and who is not. Within the causal inference framework, this design is considered among the most credible nonexperimental strategies because it relies on relatively weak and easy. How to do regression discontinuity and interpret results. Randomized controlled trials in a regression discontinuity framework. We use real data from the carolina abecedarian project to show how an analysis of an rdd can be performed from start to finish. Using ive to recover the treatment effect in a quasiexperiment. This presentation focused on the theory, design, and implementation of regression discontinuity rdd. Religion see three way hierarchical log linear analysis. Editorial full text access special issue editors introduction. Regressiondiscontinuity designs in medical settings.
You can view the design for any of the dialogs and you can customize them using the. The analysis of the regressiondiscontinuity design in r. Cattaneoy luke keelez roc o titiunikx gonzalo vazquezbarefebruary 4, 2015 abstract we consider a regression discontinuity design where the treatment is received if a score is above a cuto, but the cuto may vary for each unit in the sample instead of. When the assignment variable is a random number that is generated by the researcher, the regression discontinuity design is equivalent to a randomized controlled trial. Regression discontinuity rd analysis is a rigorous nonexperimental1 approach that can be used to estimate program impacts in situations in which candidates are selected for treatment based on whether their value for a numeric rating exceeds a designated threshold or cutpoint. Owen ozier the world bank regression discontinuity 6 may 2015 2 9. A practical guide to regression discontinuity mdrc. Logistic2x2x2 see binary logistic regression with spss. A regression discontinuity rd research design is appropriate for program evaluation problems in which treatment status or the probability of treatment depends. The regression discontinuity designtheory and applications. Statistical power for regression discontinuity designs in. Regression discontinuity rd is a twogroup, prepost, quasiexperimental method for evaluating the effect of an intervention.
Regression discontinuity designs in stata matias d. The index must rank people or units in a continuous or smooth. Assignment to the experimental or comparison group. Estimating causal effects using a regressiondiscontinuity approach stata textbook examples.
Graphical presentation of regression discontinuity results. Regression discontinuity designs in economics david s. Program on children, economics of education program, labor studies program in regression discontinuity rd designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an. It provides point estimators, confidence intervals estimators, bandwidth selectors, automatic rd plots, and other related features. Pdf regression discontinuity designs in healthcare research. Regrdiscont see using spss to analyze data from a regressiondiscontinuity design. Regression discontinuity world bank sief aphrc impact. Analyzing data from a regression discontinuity study quantitative. Regression discontinuity design rdd is a quasiexperimental evaluation option that measures the impact of an intervention, or treatment, by applying a treatment assignment mechanism based on a continuous eligibility index which is a variable with a continuous distribution. Recent advances and applications the main reference for the lectures is d. Regression discontinuity design in gifted and talented.
The extension commands can be run from spss statistics command syntax in the same. Part 2 explains how in theory a regression discontinuity analysis can identify an average effect of treatment for a population and how different types of regression discontinuity analyses sharp versus fuzzy can identify average. When the circumstances are right, regression discontinuity can be an excellent way to extract causal estimates from observational data. You can use these procedures for business and analysis projects where ordinary regression techniques are limiting or inappropriate. Plan of the three lectures on regression discontinuity designs. Discontinuity, stats rdd, regression discontinuity analysis.
Regression discontinuity world bank sief aphrc impact evaluation training 2015 owen ozier development research group the world bank 6 may 2015 owen ozier the world bank regression discontinuity 6 may 2015 1 9. Regression discontinuity design in stata part 1 stata. I would like to implement a fuzzy regression discontinuity design. The full commaseparated csv data file can be downloaded at. The regression discontinuity design is a statistical approach that utilizes threshold based decision making to derive compelling causal estimates of different interventions. Identi cation in regression discontinuity designs with.
In this video i give you a prototypical situation where rd. The regressiondiscontinuity design builds on the preexperimental static group comparison design by introducing a series of comparison groups, addressing the issue of selection as a potential rival explanation. The regression discontinuity design rdd can be used to estimate the impact of an intervention in cases in which a randomized controlled trial rct is not feasible. The analyses were done using spss for windows with three separate. In a regression discontinuity design, we measure the. See cook 2008 for a thorough account of the multidisciplinary history of rdd. All of the r extensions include a custom dialog and an extension command. Regrdiscont see using spss to analyze data from a regression discontinuity design. This work was supported by the national science foundation. To apply a regression discontinuity design, the following main conditions must be met. The adobe flash plugin is needed to view this content. Ibm spss regression enables you to predict categorical outcomes and apply various nonlinear regression procedures. Published in volume 6, issue 4, pages 1004 of american economic journal.
Examine a scatter plot to see if any discontinuity is apparent at the cutoff. The accepted recommendation is to use local linear regression, as opposed to standard nonparametric kernal regression. The extension commands can be run from spss statistics command syntax in the same manner as any builtin. The spss syntax used for testing the model differences in hypothesis 1 and hypothesis 2 is found. The regression discontinuity design rdd performs well across multiple. Ibm fuzzy regression discontinuity designs twostage. Article information, pdf download for regression discontinuity and beyond.
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