Difference between revisions of "Quasi-Experimental Methods"

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==Assumptions and Limitations==
 
==Assumptions and Limitations==
 
In general, quasi-experimental methods require larger samples than experimental methods. Further, for quasi-experimental methods to provide valid and unbiased estimates of program impacts, researchers must make more assumptions about the control group than in experimental methods. For example, difference-in-differences relies on the equal trends assumption (see [[Difference-in-Differences]] for more details), while matching assumes identical unobserved characteristics between the treatment and control groups.
 
In general, quasi-experimental methods require larger samples than experimental methods. Further, for quasi-experimental methods to provide valid and unbiased estimates of program impacts, researchers must make more assumptions about the control group than in experimental methods. For example, difference-in-differences relies on the equal trends assumption (see [[Difference-in-Differences]] for more details), while matching assumes identical unobserved characteristics between the treatment and control groups.
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== Additional Resources ==
 
== Additional Resources ==
 
* Robert Michael's slides on [http://www.indiana.edu/~educy520/sec6342/week_05/quasi_designs_2up.pdf Quasi-Experimental Designs]
 
* Robert Michael's slides on [http://www.indiana.edu/~educy520/sec6342/week_05/quasi_designs_2up.pdf Quasi-Experimental Designs]

Latest revision as of 07:03, 12 February 2020

Quasi-experimental methods are research designs that that aim to identify the impact of a particular intervention, program or event (a "treatment") by comparing treated units (households, groups, villages, schools, firms, etc.) to control units. While quasi-experimental methods use a control group, they differ from experimental methods in that they do not use randomization to select the control group. Quasi-experimental methods are useful for estimating the impact of a program or event for which it is not ethically or logistically feasible to randomize. This page outlines common types of quasi-experimental methods.

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Overview

Like experimental methods, quasi-experimental methods aim to estimate program effects free of confoundedness, reverse causality or simultaneous causality. While quasi-experimental methods use a counterfactual, they differ from experimental methods in that they do not randomize treatment assignment. Instead, quasi-experimental methods exploit existing circumstances in which treatment assignment has a sufficient element of randomness, as in regression discontinuity design or event studies; or simulate an experimental counterfactual by constructing a control group as similar as possible to the treatment group, as in propensity score matching. Other examples of quasi-experimental methods include instrumental variables and difference-in-differences.

Assumptions and Limitations

In general, quasi-experimental methods require larger samples than experimental methods. Further, for quasi-experimental methods to provide valid and unbiased estimates of program impacts, researchers must make more assumptions about the control group than in experimental methods. For example, difference-in-differences relies on the equal trends assumption (see Difference-in-Differences for more details), while matching assumes identical unobserved characteristics between the treatment and control groups.

Additional Resources