Secondary data is data collected by any party other than the researcher, including administrative data from programs, geodata from specialized sources, and census or other population data from governments.
Primary data collection is the process of gathering data through surveys, interviews, or experiments. A typical example of primary data is household surveys.
Randomization is a critical step for ensuring exogeneity in experimental methods and randomized control trials (RCTs).
Experimental methods are research designs in which the researcher explicitly and intentionally induces exogenous variation in the intervention assignment to facilitate causal inference. Experimental methods typically include directly randomized variation of programs or interventions.
Randomization is a critical step for ensuring exogeneity in experimental methods and randomized control trials (RCTs).
High Frequency Checks (HFCs) are repeated quality control procedures conducted during data collection to ensure data integrity, identify errors early, and allow corrective actions before the fieldwork is complete. HFCs should be conducted every time new data is collected to provide timely feedback to both the field team and the research team. To support this process, DIME Analytics developed iehfc as an R package to help standardize and simplify high-frequency checks according to best practices.
During surveys, you might often need to randomize various aspects of the questionnaire. While SurveyCTO has a random number generator, is is usually not recommended that you use it.
A survey firm term of reference (TOR) defines the structure of the project and breaks down the responsibilities of all parties involved, including that of the impact evaluation team.
Enumerator training is an extremely important part of the primary data collection, and should be planned in advance.