Software & Tools
IPA and J-PAL staff members and researchers use a number of software programs to design randomized evaluations and analyze the data from them. This section contains resources – both internally-developed and external ones – to help users use some of
Software & Tools
IPA and J-PAL staff members and researchers use a number of software programs to design randomized evaluations and analyze the data from them. This section contains resources – both internally-developed and external ones – to help users use some of this software.
Optimal Design is a free program for conducting power calculations, compatible only with Windows operating systems (for advice on running Optimal Design on Macs, see here.) The program and documentation are available for download here.
- Guide to Doing Power Calculations with examples from Optimal Design Software. This guide explains the trade-offs in designing a well powered randomized trial. It contains exercises and a step-by-step guide that demonstrates how varying parameters can affect the statistical power of a study.
Stata is a data analysis and statistical software program, compatible with both Windows and Mac operating systems. It is widely used by economists and a number of technical resources developed at IPA and J-PAL have been designed with Stata users in mind. For more information, see here.
- All versions of Stata come with a user guide and manuals—working through the user guide is a very efficient way to learn the basics of Stata
- Writing Randomization Code in Stata: A Guide. This step-by-step guide uses data and an annotated Stata do-file to illustrate how a simple randomization can be carried out using Stata.
- Power calculations in Stata: A Guide. This step-by step guide uses data and an annotated Stata do-file to illustrate how power calculations can be carried out using Stata. It provides an example of both a conventional parametric and a non-parametric simulation method of calculating power.
- The UCLA Institute for Digital Research and Education provides a number of valuable resources on learning and using Stata, organized by topic. The search functionality allows for looking up resources on specific commands e.g. this page on using the “egen” command to recode continuous variables into groups.
- Ray Kluender and Ben Marx have a number of hands-on tips and suggestions for working with data in Stata.
- Germán Rodriguez has a short introduction to Stata for new users, with an emphasis on data management and graphics, available here.
- Data Science Central Data Science Central has created a few "cheat sheets" on using Stata for data science tasks and analysis. These will be of interest to both novice and advanced Stata users.
- The randtreat command performs random treatment assignment. It can handle an arbitrary number of treatments and uneven treatment fractions, which are common in real-world randomized control trials. It also provides several methods to deal with'misfits', a practical issue that arises in