|
|
| (40 intermediate revisions by 5 users not shown) |
| Line 1: |
Line 1: |
| == Read First ==
| | For information on sampling approaches, please see [[Sampling]]. For information on sample size and power calculations, see [[Sample Size and Power Calculations]]. |
| 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_Sets|Master data set]]. If no master data set exist for the [[Unit_of_Observations|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 ===
| |
| | |
| when creating a population frame, also called sampling fram, you should always work from a [[Master_Data_Sets|master data set]]. If you do not have a master data set for the [[Unit_of_Obsrevation|unit of observation]] you are sampling from (for example, households, villages, clinics, schools) you should start by creating one.
| |
| | |
| 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 [[Randomization in Stata|reproducible randomization in Stata]] for details.
| |
| | |
| == Additional Resources ==
| |
| | |
| * [http://unstats.un.org/unsd/demographic/sources/surveys/Series_F98en.pdf Designing Household Survey Samples: Practical Guidelines] United Nations, Department of Economic and Social Affairs, Statistics Division - 2008
| |
