Difference between revisions of "Experimental Methods"

Jump to: navigation, search
Line 13: Line 13:
 
== Common Types of Experimental Methods ==
 
== Common Types of Experimental Methods ==
  
Experimental methods in [[impact evaluation design]] often include directly randomized variation of programs or interventions offered to study populations. This variation is usually broadly summarized as "[[Randomized Control Trials]]", but can include cross-unit variation with one or more periods ([[cross-sectional]] or [[difference-in-difference]] designs); within-participant variation ([[panel]] studies); or treatment randomization at a clustered level with further variation within clusters (multi-level), for example.
+
Experimental methods in [[impact evaluation design]] often include directly randomized variation of programs or interventions offered to study populations. This variation is usually broadly summarized as "[[Randomized Control Trials]]", but can include cross-unit variation with one or more periods ([[cross-sectional]] or [[difference-in-difference]] designs); within-participant variation ([[panel]] studies); or treatment randomization at a [[Randomized Control Trials#Clustered RCTs | clustered]] level with further variation within clusters (multi-level), for example.
  
 
Experimental variation is also possible on the research side through randomized variation in the survey methodology. For example,  public health surveys have used "[[mystery patients]]" to identify the quality of medical advice given to people in primary care settings; by comparing the outcomes with other health care providers given [[medical vignettes]] instead of mystery patients, or by changing the information given from the patient to the provider, or by changing the setting in which the interaction is conducted, causal differences in outcomes can be estimated.
 
Experimental variation is also possible on the research side through randomized variation in the survey methodology. For example,  public health surveys have used "[[mystery patients]]" to identify the quality of medical advice given to people in primary care settings; by comparing the outcomes with other health care providers given [[medical vignettes]] instead of mystery patients, or by changing the information given from the patient to the provider, or by changing the setting in which the interaction is conducted, causal differences in outcomes can be estimated.
  
 
Additionally, designs like [[endorsement experiments]] and [[list experiments]] randomly vary the contents of the survey itself to elicit accurate responses from participants when there is concern about [[social desirability bias]] or [[Hawthorne effects]].
 
Additionally, designs like [[endorsement experiments]] and [[list experiments]] randomly vary the contents of the survey itself to elicit accurate responses from participants when there is concern about [[social desirability bias]] or [[Hawthorne effects]].

Revision as of 02:03, 9 February 2018

Experimental methods are research designs in which the investigator explicitly and intentionally induces exogenous variation in the uptake of the program to be evaluated. Experimental methods, such as Randomized Control Trials, are typically considered the gold standard design for impact evaluation, since by construction the takeup of the treatment is uncorrelated with other characteristics of the treated population. Under these conditions, it is always possible for the analyst to construct a regression model in which the estimate of the treatment effect is unbiased.

The Power of Experimental Methods

Specifically, non-experimental methods leave the researcher with a dataset where an unknown combination of factors are involved in the true data-generating process of the outcome variable of interest. Estimating marginal effects of any of these factors on the outcome itself (such as schooling on earnings, for example), leaves open two key avenues for biased estimates.

First, the estimate may be confounded, in the sense that it masks an effect produced reality by another, correlated variable. For example, schooling may improves the quality of job offers via network exposure, but the actual education adds no value. In this case the result remains "correct" in the sense that those who got more schooling got higher earnings, but "incorrect" in the sense that the estimate is not the marginal value of education.

Second, the direction of causality may be reversed or simultaneous. For example, individuals who are highly motivated may choose to complete more years of schooling as well as being more competent at work in general; or those who are highly motivated by financial returns in the workplace may choose more schooling because of that motivation.

Experimental variation solves these problems by imposing a known variation on the study population. This guarantees that the intervention effect is not confounded (since it is not correlated with any external variable) and that causality is identified, since selection into the randomization is not possible. However, this leads to natural concerns about the structure of differential takeup and attrition in a randomization setting which must be addressed in every sample where noncompliance is a possibility.

Common Types of Experimental Methods

Experimental methods in impact evaluation design often include directly randomized variation of programs or interventions offered to study populations. This variation is usually broadly summarized as "Randomized Control Trials", but can include cross-unit variation with one or more periods (cross-sectional or difference-in-difference designs); within-participant variation (panel studies); or treatment randomization at a clustered level with further variation within clusters (multi-level), for example.

Experimental variation is also possible on the research side through randomized variation in the survey methodology. For example, public health surveys have used "mystery patients" to identify the quality of medical advice given to people in primary care settings; by comparing the outcomes with other health care providers given medical vignettes instead of mystery patients, or by changing the information given from the patient to the provider, or by changing the setting in which the interaction is conducted, causal differences in outcomes can be estimated.

Additionally, designs like endorsement experiments and list experiments randomly vary the contents of the survey itself to elicit accurate responses from participants when there is concern about social desirability bias or Hawthorne effects.