Difference between revisions of "Quasi-Experimental or Non-Experimental Designs"

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(New page: *<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the...)
 
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*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is ''propensity score matching'', in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.<o:p></o:p></font></span>
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*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Matching methods or constructed controls'', in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is ''propensity score matching'', in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.</font></span>  
*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Double difference or difference-in-differences ''methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.<o:p></o:p></font></span>
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*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Double difference or difference-in-differences ''methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.</font></span>  
*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Instrumental variables or statistical control ''methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.<o:p></o:p></font></span>
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*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Instrumental variables or statistical control ''methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation </font></span><span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.</font></span>  
*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Reflexive comparisons'', in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.</font></span><span style="mso-bidi-font-family: arial; mso-bidi-font-size: 10.0pt"><o:p></o:p></span>
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*<span style="font-size: 10pt; mso-bidi-font-family: arial"><font face="Arial">''Reflexive comparisons'', in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.</font></span>

Revision as of 13:17, 3 November 2009

  • Matching methods or constructed controls, in which one tries to pick an ideal comparison that matches the treatment group from a larger survey. The most widely used type of matching is propensity score matching, in which the comparison group is matched to the treatment group on the basis of a set of observed characteristics or by using the “propensity score” (predicted probability of participation given observed characteristics); the closer the propensity score, the better the match. Agood comparison group comes from the same economic environment and was administered the same questionnaire by similarly trained interviewers as the treatment group.
  • Double difference or difference-in-differences methods, in which one compares a treatment and comparison group (first difference) before and after a program (second difference). Comparators should be dropped when propensity scores are used and if they have scores outside the range observed for the treatment group.
  • Instrumental variables or statistical control methods, in which one uses one or more variables that matter to participation but not to outcomes given participation. This identifies the exogenous variation in outcomes attributable to the program, recognizing that its placement is not random but purposive. The “instrumental variables” are first used to predict program participation; then one sees how the outcome indicator varies with the predicted values.
  • Reflexive comparisons, in which a baseline survey of participants is done before the intervention and a follow-up survey is done after. The baseline provides the comparison group, and impact is measured by the change in outcome indicators before and after the intervention.