11

Apparently this is called attrition bias. It's very similar to survivorship bias. This paper suggest correcting for it using Heckman correction. Propensity score matching may also help somewhat. My experience with both has been mixed, but they are commonly used. You should figure out what exact approach is most appropriate for your setting. One last edit: ...


6

I think this paper might be useful to you. It's a job market paper by one of Heckman's students at UChicago, named Rodrigo Pinto. The paper is titled "Selection Bias in a Controlled Experiment: The Case of Moving to Opportunity." In the MTO experiment, the voucher assignment mechanism was random but only approximately half that received the voucher ended up ...


4

Another thing you can look at is "Intention-to-treat analysis". From Wikipedia, An intention-to-treat (ITT) analysis of the results of an experiment is based on the initial treatment assignment and not on the treatment eventually received. ITT analysis is intended to avoid various misleading artifacts that can arise in intervention research such as non-...


3

Without having read the full piece I'd say you are mostly spot on, but for a subtle difference. The objection runs a bit deeper than your rephrasing of it, where you refer to single estimate. The thing is that there are way more than a single estimate this applies to, and therefore you are more likely to be off. The point is that for RCT to establish ...


2

The fact that you can only encourage but not force the teachers to participate puts you squarely in the world of Local Average Treatment Effect (LATE) and Intention to Treat analysis. Essentially if you use the voucher as a instrumental variable, you will get an estimate of the effect of the voucher on "the sort of person who you can encourage to take a ...


2

Ok, I am far from an econometrician, but my train of thought would be as follows: By using random assignment we have two groups that are on average equal to one another on all aspects. As you rightly point out due to sampling variation there will be differences. My worry would be that if I start "correcting" those after randomization (as per your example ...


1

Yes, you can consider this as panel data, but the key is to understanding why, as this affects how you explain and interpret your panel regression. In treating each cohort as the same unit over time, you are assuming that each cohort has a constant, unobserved fixed effect. That is, people in a particular age category and gender tend to have an unobserved ...


1

The idea is that for causal inference you want to be able to treat the control group as a counterfactual. In the ideal world you would wanna be able to observe parallel world where the same people who you give treatment don’t get one. Now simple random sample promises to deliver this because as long as everyone has really random and most importantly equal ...


1

I've worked in 2015 on a survey collecting ~300 answers from 130 different countries. We had similar requirements to what you described, with multilanguage functionality on top. it was not a RCT (Randomized Controlled Trial), we were surveying on conditions of work. We ended up using SurveyMonkey which costed 900€ and had only the online functionality, ...


1

To answer your first question: it depends on the subsample that you want to use. A representative or stratified sample is constructed by dividing the population of interest into non-overlapping subsets, drawing a random sample from each subset, and then computing weights to adjust for the fact that not all elements from the sample had the same probability ...


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