Primary Data Collection

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

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  • The DIME Research Standards provide a comprehensive checklist to ensure that collection and handling of research data is in line with global best practices.
  • Personal interviews are one of the most effective medium for primary data collection. Depending on the research question, these interviews may take the form of household surveys, business (firm) surveys, or agricultural (farm) surveys.
  • iefieldkit is a Stata package that aids primary data collection. It currently supports three major components of that workflow: survey design; survey completion; and data-cleaning and survey harmonization.


While impact evaluations often benefit from secondary sources of data like administrative data, census data, or household data, these sources may not always be available. In such cases, researchers need to collect data directly through a series of well-designed interviews and surveys. The process of collecting primary data requires a great deal of foresight, planning and coordination. Listed below are the crucial steps involved the in preparation and collection of primary data.

Pre-register research

The first step with any new research project is to pre-register your research, including the methodology, and draft a pre-analysis plan.

Acquire approval from human subjects

There are strict rules about acquiring approval from human subjects. Researchers must understand the ethics and rules for security of sensitive data, and should use proper tools for encryption and de-identification of personally identifiable information (PII).

Compile the survey budget

Researchers must prepare a survey budget before procuring a survey firm. This step allows researchers to calculate expected costs of conducting a study, and compare these with the proposals of firms that submit an expression of interest (EOI).

Determine relevant parameters of a study

After agreeing upon a budget, researchers then decide upon factors like the adequate sampling frame (which is a list of individuals or units in a population from which a sample can be drawn), sample size, and statistical power based on which they can then randomize treatment.

Procure a survey firm

The next step is to procure a survey firm after issuing detailed terms of reference (TOR), and performing due diligence among local research firm options.

Carry out a pre-pilot

The first stage of the survey pilot, the pre-pilot involves two things: piloting content, and piloting protocols. Clear protocols allow researchers to ensure that field collection is carried out consistently across teams and/or regions, and ensure that published research is reproducible.

Refine and review the survey design

The first stage of the survey pilot allows researchers to develop a design for the instrument. The researchers then conduct the second stage of the survey pilot, called content-focused pilot, to review and refine the structure of the instrument.

Translate the survey instrument

After the content-focused pilot, the research firm translates the instrument into all local languages. This step helps to ensure that the survey can be taken by more people, therefore making the study more effective.

Program the instrument

After obtaining the IRB Approval, researchers program the questionnaire. This step makes it easier to share surveys that rely on methods like Computer-Assisted Personal Interviews (CAPI) or Computer-Assisted Field Entry (CAFE)
Also refer to SurveyCTO coding practices to learn more about programming surveys.

Train enumerators and monitor data quality

After validating the programming of the questionnaire, the researchers train enumerators, and monitor data-quality to generate a final draft of the instrument. Monitoring can be done in the form of back checks, high frequency checks, as well as other methods.

Maintain an organized data folder

DIME has created a Stata package, iefolder. Part of the DIME Analytics ietoolkit, this package helps increase project efficiency, and reduces the risk of error in a study.

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