Difference between revisions of "Primary Data Collection"

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*[[Monitoring Data Quality | Monitor]] data quality through [[Back Checks | backchecks]], high frequency checks, and other methods.
 
*[[Monitoring Data Quality | Monitor]] data quality through [[Back Checks | backchecks]], high frequency checks, and other methods.
 
*Maintained an organized data folder via <code>[[iefolder]]</code>.
 
*Maintained an organized data folder via <code>[[iefolder]]</code>.
*[[Data Cleaning |Clean]] and [[Data Analysis | analyze]] data.
 
  
 
== Back to Parent ==
 
== Back to Parent ==

Revision as of 16:13, 17 March 2020

Primary data collection is the process of gathering data through surveys, interviews or experiments. Household surveys are the prototypical example of primary data. 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:

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This article is part of the topic Primary Data Collection

Additional Resources

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.