Difference between revisions of "Sampling & Power Calculations"
Mrijanrimal (talk | contribs) |
Mrijanrimal (talk | contribs) |
||
| Line 21: | Line 21: | ||
=== Stratification === | === Stratification === | ||
To ensure a representative sample you can use stratification. | To ensure a representative sample you can use stratification. A typical variable to stratify on is gender. When you stratify on gender you guarantee that your sample has the same ratio of women as the population frame you are sampling from. | ||
=== Randomization in Stata === | === Randomization in Stata === | ||
Revision as of 14:47, 2 February 2017
Read First
Sampling is a task that is impossible to do differently once the results have been used in the field. Therefore, always ask a second person to go over your code before you use the sampling it generated in the field. For DIME projects, you should always consult any member of DIME Analytics before sending a sample to the field.
- Do not randomize the sample from a temporary data set or a data set constructed for only this purpose. Instead, always randomize from a Master data set. If no master data set exist for the unit of observation you are sampling on, then it is very important that you start by creating that.
Guidelines
Power Calculations
Power Calculations are a statistical tool to estimate either sample size or minimum detectable effect. Which you should estimate depends on the research design and constraints of a specific impact evaluation. The types of questions you can answer through power calculations include:
- Given that I want to be able to statistically distinguish program impact of a 10% change in my outcome of interest, what is the minimum sample size needed?
- Given that I only have budget to sample 1,000 households, what is the minimum effect size that I will be able to distinguish from a null effect?
Population Frame
This section discuss how to
First you need a population frame to sample from.
Stratification
To ensure a representative sample you can use stratification. A typical variable to stratify on is gender. When you stratify on gender you guarantee that your sample has the same ratio of women as the population frame you are sampling from.
Randomization in Stata
All code work you produce should be reproducible. Any code that includes randomization needs version, seed and sort to be reproducible. See reproducible randomization in Stata for details.
Additional Resources
- Designing Household Survey Samples: Practical Guidelines United Nations, Department of Economic and Social Affairs, Statistics Division - 2008