B DIME Analytics Resource Directory
The resources listed in this appendix are mentioned throughout this book. This appendix provides them in one place for easy reference. All of these resources are made public under generous open-source licenses. This means that they are free to use, reuse, and adapt for any purpose, so long as they are cited appropriately.
Public resources and tools
DIME Wiki. One-stop shop for impact evaluation research solutions. The DIME Wiki is a resource focused on practical implementation guidelines rather than theory, open to the public, easily searchable, suitable for users of varying levels of expertise, up-to-date with the latest technological advances in electronic data collection, and curated by a vibrant network of editors with expertise in the field. Hosted at https://dimewiki.worldbank.org.
Stata Visual Library. A curated, easy-to-browse selection of graphs created in Stata. Clicking on each graph reveals the source code, allowing for easy replication. Hosted at https://worldbank.github.io/stata-visual-library.
R Econ Visual Library. A curated, easy-to-browse selection of graphs created in R. Clicking on each graph reveals the source code, allowing for easy replication. Hosted at https://worldbank.github.io/r-econ-visual-library.
DIME Analytics Research Standards. A repository outlining DIME’s public commitments to research ethics, transparency, reproducibility, data security, and data publication, along with supporting tools and resources. Hosted at https://github.com/worldbank/dime-standards.
Flagship training courses
Manage Successful Impact Evaluations (MSIE). The flagship training of DIME Analytics. MSIE is a week-long annual course, held in person in Washington, DC. MSIE is intended to improve the skills and knowledge of impact evaluation practitioners, familiarizing them with critical issues in impact evaluation implementation, recurring challenges, and cutting- edge technologies. The course consists of lectures and hands-on sessions. Through small group discussions and interactive computer lab sessions, participants work together to apply what they have learned and have an opportunity to develop their skills. Hands-on sessions are offered in parallel tracks, with different options based on software preferences and skill level. Course materials available at https://osf.io/h4d8y.
Manage Successful Impact Evaluation Surveys (MSIES). A fully virtual course in which participants learn the workflow for primary data collec- tion. The course covers best practices at all stages of the survey workflow, from planning to piloting instruments and monitoring data quality once fieldwork begins. There is a strong focus throughout on research ethics and reproducible workflows. The course uses a combination of virtual lectures, case studies, readings, and hands-on exercises. Course materials available at https://osf.io/resya.
Research Assistant Onboarding Course. A course designed to familiarize research assistants and research analysts with DIME’s standards for data work. By the end of the course’s six sessions, participants have the tools and knowledge to implement best practices for transparent and reproducible research. The course focuses on how to set up a collabora- tive workflow for code, data sets, and research outputs. Most content is platform-independent and software-agnostic, but participants are expected to be familiar with statistical software. The course materials are available at https://osf.io/qtmdp.
Introduction to R for Advanced Stata Users. An introduction to the R programming language, building on knowledge of Stata. The course focuses on common tasks in development research related to descriptive analysis, data visualization, data processing, and geospatial data work. Materials available at https://osf.io/nj6bf.
DIME Analytics Trainings. The DIME Analytics homepage on the Open Science Framework includes links to materials for all past courses and technical trainings. Materials available at https://osf.io/wzjtk.
Software tools and trainings
ietoolkit. A suite of Stata commands to routinize common tasks for data
management and impact evaluation analysis. Developed at
iefieldkit. A suite of Stata commands to routinize and document common tasks
in primary data collection. Developed at
DIME Analytics GitHub Trainings and Resources. A GitHub repository containing all the GitHub training materials and resources developed by DIME Analytics. The trainings follow DIME’s model for organizing research teams on GitHub and are designed for face-to-face delivery, but materials are shared so that they may be used and adapted by others. Hosted at https://github.com/worldbank/dime-github-trainings.
DIME Analytics LaTeX Training. A user-friendly guide to getting started with LaTeX. Exercises provide opportunities to practice creating appen- dixes, exporting tables from R or Stata to LaTeX, and formatting tables in LaTeX. Available at https://github.com/worldbank/DIME-LaTeX-Templates.