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. 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.

Read First

• 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 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.

Guidelines

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 therefore should use proper tools for encryption and de-identification of personally identifiable information (PII).

Compile the survey budget

The survey budget must be prepared before hiring a survey firm. This step allows researchers to calculate expected costs of conducting a study, and compare these with the proposals submitted by survey firms.

Determine relevant parameters of a study

Based on the available funding, researchers can 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, statistical power based on which they can then randomize treatment.

Design and translate the survey instrument

The next step in the process is to then design and translate the survey instrument, which is often in the form of a questionnaire.

Program the instrument

Once the [[Survey Pilot|piloting of the instrument] is complete, and design has been finalized, the next step is to program the questionnaire (the survey instrument).
Also refer to these guidelines for coding/programming the instrument using SurveyCTO.

Establish survey protocols

Establish survey protocols.

Pilot the survey instrument

Pilot the survey instrument–both the content and protocols.

Procure a survey firm

Procure a survey firm, taking care to prepare detailed Terms of Reference.

Train enumerators

Train enumerators

Monitor data quality

Monitor data quality can be done through back-checks, high frequency checks, and other methods.

Maintain an organized data folder

via iefolder.

Back to Parent

This article is part of Primary Data Collection

Additional Resources

• Brief from Oxfam: Planning Survey Research
• DIME Guide on Planning, 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.