# Sampling & Power Calculations

Creating a statistically valid sample representative of the population of interest for the impact evaluation is a crucial aspect of impact evaluation design. This task can be roughly divided into two phases: sample design and implementation. Implementation typically means writing a software program to enact the sampling strategy. Sampling code requires extra care! Errors cannot be corrected after the intervention (or survey) has started. Always ask a second person to doublecheck 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

### Sample Design

#### 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 (Sampling Frame)

What is the population of interest for the impact evaluation? In other words, what population does your sample need to represent? This will vary depending on the study design. Some data on the overall population is required, in order to draw a representative sample.

#### 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.

### Sample Selection

#### Population Frame

When creating a population frame, also called sampling frame, you should always work from a master data set. If you do not have a master data set for the unit of observation you are sampling from (for example, households, villages, clinics, schools) you should always start by creating one.

First you need a population frame to sample 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.