• Development Research in Practice
    The DIME Analytics Data Handbook
  • Welcome
    • Published by
      DIME Analytics
  • Foreword
  • Acknowledgements
  • About the authors
  • Feedback
    • How to contribute
    • Already addressed errata
  • Abbreviations
  • Introduction
    • How to read this book
    • The DIME Wiki: A complementary resource
    • Standardizing data work
    • Standardizing coding practices
    • The team behind this book
    • Looking ahead
  • 1 Conducting reproducible, transparent, and credible research
    • Developing a credible research project
      • Registering research
      • Writing preanalysis plans
      • Publishing registered reports
    • Conducting research transparently
      • Documenting data acquisition and analysis
      • Cataloging and archiving data
    • Analyzing data reproducibly and preparing a reproducibility package
    • Looking ahead
  • 2 Setting the stage for effective and efficient collaboration
    • Preparing a collaborative work environment
      • Setting up a computer for data work
      • Establishing effective documentation practices
      • Setting up a code environment
    • Organizing code and data for replicable research
      • Organizing files and folders
      • Establishing common file formats
      • Using version control
      • Writing code that others can read
      • Writing code that others can run
      • BOX 2.6 WRITING CODE THAT OTHERS CAN RUN: A CASE STUDY FROM THE DEMAND FOR SAFE SPACES PROJECT
    • Preparing to handle confidential data ethically
      • Seeking ethical approval
      • Obtaining informed consent
      • BOX 2.7 SEEKING ETHICAL APPROVAL: AN EXAMPLE FROM THE DEMAND FOR SAFE SPACES PROJECT
      • BOX 2.8 OBTAINING INFORMED CONSENT: A CASE STUDY FROM THE DEMAND FOR SAFE SPACES PROJECT
      • Ensuring research subject privacy
      • BOX 2.9 ENSURING THE PRIVACY OF RESEARCH SUBJECTS: AN EXAMPLE FROM THE DEMAND FOR SAFE SPACES PROJECT
    • Looking ahead
  • 3 Establishing a measurement framework
    • Documenting data needs
      • Developing a data linkage table
      • Constructing master data sets
      • Creating data flowcharts
    • Translating research design to data needs
      • Applying common research designs to data
      • Including multiple time periods
      • Incorporating monitoring data
    • Creating research design variables by randomization
      • Randomizing sampling and treatment assignment
      • Programming reproducible random processes
      • Implementing clustered or stratified designs
      • Performing power calculations
    • Looking ahead
  • 4 Acquiring development data
    • Acquiring data ethically and reproducibly
      • Determining data ownership
      • Obtaining data licenses
      • Documenting data received from partners
    • Collecting high-quality data using electronic surveys
      • Designing survey instruments
      • Piloting survey instruments
      • Programming electronic survey instruments
      • Using electronic survey features to enhance data quality
      • Training enumerators
      • Checking data quality in real time
    • Handling data securely
      • Encrypting data
      • Collecting and storing data securely
      • Backing up original data
    • Looking ahead
  • 5 Cleaning and processing research data
    • Making data “tidy”
      • Establishing a unique identifier
      • Tidying data
    • Implementing data quality checks
      • BOX 5.4 ASSURING DATA QUALITY: A CASE STUDY FROM THE DEMAND FOR SAFE SPACES PROJECT
    • Processing confidential data
      • Protecting research subject privacy
      • Implementing de-identification
    • Preparing data for analysis
      • Exploring the data
      • Correcting data points
      • Recoding and annotating data
      • Documenting data cleaning
    • Looking ahead
  • 6 Constructing and analyzing research data
    • Creating analysis data sets
      • Organizing data analysis workflows
      • Integrating multiple data sources
      • Creating analysis variables
      • Documenting variable construction
    • Writing analysis code
      • Organizing analysis code
      • Visualizing data
    • Creating reproducible tables and graphs
      • Managing outputs
      • Exporting analysis outputs
    • Increasing efficiency of analysis with dynamic documents
      • Conducting dynamic exploratory analysis
      • Using LaTeX for dynamic research outputs
    • Looking ahead
  • 7 Publishing reproducible research outputs
    • Publishing research papers and reports
      • Using LaTeX for written documents
      • Getting started with LaTeX as a team
    • Preparing research data for publication
      • De-identifying data for publication
      • Publishing research data sets
    • Publishing a reproducible research package
      • Organizing code for reproducibility
      • Releasing a reproducibility package
    • Looking ahead
  • Conclusion
    • Bringing it all together
    • Where to go from here
  • Appendix
  • A The DIME Analytics Coding Guide
    • Writing good code
    • Using the code examples in this book
    • The DIME Analytics Stata Style Guide
      • Commenting code
      • Abbreviating commands
      • Abbreviating variable names
      • Writing loops
      • Using white space
      • Writing conditional expressions
      • Writing file paths
      • Using line breaks
      • Using boilerplate code
      • Miscellaneous notes
  • B DIME Analytics Resource Directory
    • Public resources and tools
    • Flagship training courses
    • Software tools and trainings
  • C Research design for impact evaluation
    • Understanding causality, inference, and identification
      • Estimating treatment effects using control groups
      • Designing experimental and quasi-experimental research
    • Obtaining treatment effects from specific research designs
      • Cross-sectional designs
      • Difference-in-differences
      • Regression discontinuity
      • Instrumental variables
      • Matching
      • Synthetic control
  • Bibliography

Development Research in Practice
The DIME Analytics Data Handbook

Development Research in Practice
The DIME Analytics Data Handbook

Kristoffer Bjarkefur, Luiza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones

Welcome

Published by
DIME Analytics

Compiled from the following commit: https://github.com/worldbank/dime-data-handbook/commit/1008885582890e35fdec0bd7d83aa76a618ff8d1

Released under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.