SurveyCTO Additional Topics
This article explains and provides examples of miscellaneous topics in SurveyCTO programming that may be of some interest to users.
Read First
- Gives concrete examples of randomization in SurveyCTO to complement the overview given at the previous link
- Also suggests methods to minimize coding mistakes due to copying; how to display multiple questions during a [Survey Pilot|survey]]; and editing [SurveyCTO Programming|SurveyCTO] HTML input.
- Various other functions
Random Draw of Beneficiaries 1
This page gives a detailed example on one of the ways to perform randomization in SurveyCTO. If you would like an overview of the process, read that page first.
This example comes from an agriculture survey in Brazil, where the survey firm did a poor job in listing the members of each association. We are looking to survey 8 people in each association, but we cannot be sure that each person on the list is actually a valid member. The pools are quite large (>100) and most are assumed to be valid IDs, but we still need to be careful that there are enough IDs chosen for the list.
We approach the problem in 2 stages:
- Randomly select enough IDs from the total to be almost certain that 8 will be valid (here we choose 25)
- Have the enumerator validate that these IDs are in fact proper members of the association. If not, more IDs are pulled from the randomized list to validate until 8 are selected for participation.
Ideally, you should not be need to select participants in this manner - you would like a sample frame from which to select a sample before your survey teams go to the field to administer the household survey. But in this case, it was not possible because of a problem with the listing process.
The approach works nicely for selecting a smaller number of people, but would be rather clunky if you are looking to select, say 200 from 1,000 members. The main advantage over other methods is that you are easily able to preload the randomized numbers created in Stata or R to ensure replicability.
This approach can be altered or scaled for application in various situations. It's important to consider the number of permutations that will mostly guarantee that there are enough IDs selected once they have been de-duplicated and dropped for not being valid. If your situation is choosing from a smaller pool, or having IDs that are more likely to be dropped for not being valid, you should increase the number of draws and the questions to validate members. In this example we assume that we will be able to select 8 people from the 25 drawn IDs.