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

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− | + | 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 | sample size]] formula when conducting [[Sampling & Power Calculations | power calculations]] during the research design stage. This page provides a general overview of and additional resources for Optimal Design. | |

− | Optimal Design is free software designed by University of Michigan. It | + | |

− | |||

== Read First == | == Read First == | ||

− | Optimal Design | + | *Download Optimal Design [http://sitemaker.umich.edu/group-based/optimal_design_software here]. |

− | + | *For [[Reproducible Research | reproducibility]], DIME recommends conducting power calculations in [[Power Calculations in Stata | 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 that helps to visualize power calculations and compare, for example, power versus sample size for a given effect or effect size versus sample size for a given desired power. | ||

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

This article is part of the topic [[Sampling & Power Calculations]] | This article is part of the topic [[Sampling & Power Calculations]] | ||

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== Additional Resources == | == Additional Resources == | ||

− | [http:// | + | *Poverty Action Lab’s [https://www.ilo.org/wcmsp5/groups/public/@ed_emp/documents/presentation/wcms_419000.pdf Exercise: How to do Power Calculations in Optimal Design Software] |

+ | * Berk Ozler’s [http://blogs.worldbank.org/impactevaluations/power-calculations-what-software-should-i-use Power Calculations: What software should I use?] via The World Bank's Development Impact blog | ||

+ | *DIME Analytics guidelines on survey sampling and power calculations [https://github.com/worldbank/DIME-Resources/blob/master/survey-sampling-1.pdf 1] and [https://github.com/worldbank/DIME-Resources/blob/master/survey-sampling-2.pdf 2] | ||

+ | * Andrew Gelman’s [http://andrewgelman.com/2017/03/03/yes-makes-sense-design-analysis-power-calculations-data-collected/ Why it makes sense to revisit power calculations after data has been collected] | ||

+ | *JPAL’s [https://www.povertyactionlab.org/sites/default/files/resources/2017.01.11-The-Danger-of-Underpowered-Evaluations.pdf The Danger of Underpowered Evaluations] | ||

[[Category: Sampling & Power Calculations]] | [[Category: Sampling & Power Calculations]] |

## Revision as of 21:46, 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

- Download 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 that helps to visualize power calculations and compare, for example, power versus sample size for a given effect or effect size versus sample size for a given desired power.

## 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