Primary Data Collection

Primary data collection is the process of gathering data through surveys, interviews or experiments. A typical example of primary data is household surveys. Researchers can personally direct primary data collection to ensure that data meets the standards of quality, availability, statistical power and sampling required for a particular research question. With globally increasing access to survey tools and software, field manuals, and specialized survey firms, primary data has become the dominant source for empirical inquiry in development economics.

Read First

• Primary data collection typically requires a great deal of foresight, planning and coordination.
• DIME's Research Standards provide a comprehensive checklist to ensure that collection and handling of research data are in line with global best-practices.
• The large majority of primary data comes from personal interviews. Depending on the research, these may take the form of household surveys, business (firm) surveys, or agricultural (farm) surveys.
• iefieldkit is a Stata package for primary data collection. It currently supports three major components of that workflow: survey design; survey completion; and data cleaning and survey harmonization.

Guidelines

The following are critical steps in preparing for and conducting primary data collection:

• Pre-register research and consider producing a pre-analysis plan.
• Acquire human subjects approval and get set up with the proper tools for encryption and de-identification.
• Compile the survey budget.
• Determine the sampling frame, calculate sample size, conduct sampling and power calculations, and randomize treatment.
• Design and translate the survey instrument.
• Program the instrument if data is being collected electronically via a Computer-Assisted Personal Interviews (CAPI) or Computer-Assisted Field Entry (CAFE) survey.
• Establish survey protocols.
• Pilot the survey instrument – both the content and protocols.
• Procure a a survey firm, taking care to prepare detailed Terms of Reference.
• Train enumerators.
• Monitor data quality through backchecks, high frequency checks, and other methods.
• Maintained an organized data folder via iefolder.

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This article is part of the topic Primary Data Collection

Additional Resources

• Brief from Oxfam: Planning Survey Research
• DIME's Planning for, Preparing & Monitoring Household Surveys
• DIME Analytics’ guidelines on preparing for data collection
• Guidelines and tools for Preparing for Data Collection from the World Bank's Results Based Financing Impact Evaluation Toolkit

Oxfam provides a detailed case study of how to use electronic data collection (SurveyCTO) combined with Stata code to improve data quality in the field.