Spatial analysis involves utilizing geographical information to create descriptive and informative outputs.

reprun - This command is used to automate a reproducibility check for a single Stata do-file, or a set of do-files called by a main do-file. The command should be used interactively; reprun will execute one run of the do-file and record the state of Stata after the execution of each line. It will then run the entire do-file a second time and flag all potential reproducibility error caused by comparing the Stata state to the first run after each line. Debugging and reporting options are available.

Repkit

This Stata module is a package providing a utility toolkit for reproducibility best-practices. The motivation for this package is to make the World Bank’s reproducibility best-practices more accessible to a wider Stata community. The best-practices promoted in this package appreciated identified and implemented as part of the World Bank’s reproducibility effort.

Currently, this toolkit has the following commands:

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• Software tools are a crucial component of performing sampling and power calculations in development research.

Introduction

There is a broad range of software tools available for data analysis.

After analyzing data and before disseminating results, research teams must export analyses.

Data cleaning is an essential step between data collection and data analysis. Raw primary data is always imperfect and needs to be prepared for a high quality analysis and overall replicability.

Due to the long life span of a typical impact evaluation, multiple generations of team members often contribute to the same data work. Clear methods for organization of the data folder, the structure of the data sets in the folder, and identification of the observations in the data sets is critical.

Questionnaire design is the first step in primary data collection. A well-designed questionnaire requires planning, literature reviews of questionnaires, structured modules, and careful consideration of outcomes to measure.

Ensuring high data quality during primary data collection involves anticipating everything that can go wrong, and preparing a comprehensive data quality assurance plan to handle these issues.

Field management is the process of planning, monitoring and overseeing primary data collection activities. Correct and careful management of fieldwork activities and field staff is essential to completing data collection on time, on budget, without missing observations, and at high quality.