# Difference between revisions of "Power Calculations in Optimal Design"

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== Overview == | == Overview == | ||

− | Optimal Design creates graphs to visualize trade-offs and the relationships between the various components of power calculations. It can compare, for example, power versus sample size for a given effect or effect size versus sample size for a given desired power. While Optimal Design can aid researcher understanding and decision-making during power calculations, the software has a few shortcomings. First, it is not replicable. Second, it cannot calculate power for an individual-level randomization with binary outcome. Third, it assumes equal mean and variance for treatment and control groups. For a [[Randomized Control Trials | RCT]] this is generally okay. Fourth, it assumes an equal split of sample size or cluster number between treatment and control groups. For researchers who wish to fix the size of the treatment group, for example, due to budget constraints, and then calculate control group size, this feature is limiting. In general, [[Power Calculations in Stata | Stata]] offers much greater flexibility in power and sampling calculations. DIME Analytics strongly recommends conducting power calculations in Stata and using Optimal Design as a compliment for understanding. | + | Optimal Design creates graphs to visualize trade-offs and the relationships between the various components of power calculations. It can compare, for example, power versus sample size for a given effect or effect size versus sample size for a given desired power. While Optimal Design can aid researcher understanding and decision-making during power calculations, the software has a few shortcomings. First, it is not replicable. Second, it cannot calculate power for an individual-level randomization with binary outcome. Third, it assumes equal mean and variance for treatment and control groups. For a [[Randomized Control Trials | RCT]] this is generally okay. Fourth, it assumes an equal split of sample size or cluster number between treatment and control groups. For researchers who wish to fix the size of the treatment group, for example, due to budget constraints, and then calculate control group size, this feature is limiting. In general, [[Power Calculations in Stata | Stata]] offers much greater flexibility in power and sampling calculations. DIME Analytics strongly recommends conducting power calculations in Stata and using Optimal Design as a compliment for visualization and general understanding. |

== Back to Parent == | == Back to Parent == |

## Revision as of 22:01, 17 June 2019

Optimal Design is free software designed by University of Michigan. It provides a useful platform on which researchers can visualize the relationship between different elements of the sample size formula when conducting power calculations during the research design stage. This page provides a general overview of and additional resources for Optimal Design.

## Read First

- Access Optimal Design here.
- For reproducibility, DIME recommends conducting power calculations in Stata and using Optimal Design as a compliment for visualization.
- For more general information on power calculations, see Sampling & Power Calculations.

## Overview

Optimal Design creates graphs to visualize trade-offs and the relationships between the various components of power calculations. It can compare, for example, power versus sample size for a given effect or effect size versus sample size for a given desired power. While Optimal Design can aid researcher understanding and decision-making during power calculations, the software has a few shortcomings. First, it is not replicable. Second, it cannot calculate power for an individual-level randomization with binary outcome. Third, it assumes equal mean and variance for treatment and control groups. For a RCT this is generally okay. Fourth, it assumes an equal split of sample size or cluster number between treatment and control groups. For researchers who wish to fix the size of the treatment group, for example, due to budget constraints, and then calculate control group size, this feature is limiting. In general, Stata offers much greater flexibility in power and sampling calculations. DIME Analytics strongly recommends conducting power calculations in Stata and using Optimal Design as a compliment for visualization and general understanding.

## Back to Parent

This article is part of the topic Sampling & Power Calculations

## Additional Resources

- Poverty Action Lab’s Exercise: How to do Power Calculations in Optimal Design Software
- Berk Ozler’s Power Calculations: What software should I use? via The World Bank's Development Impact blog
- DIME Analytics guidelines on survey sampling and power calculations 1 and 2
- Andrew Gelman’s Why it makes sense to revisit power calculations after data has been collected
- JPAL’s The Danger of Underpowered Evaluations