Ensuring high data quality during primary data collection involves anticipating everything that can go wrong, and preparing a comprehensive data quality assurance plan to handle these issues.
Research Design
Field management is the process of planning, monitoring and overseeing primary data collection activities. Correct and careful management of fieldwork activities and field staff is essential to completing data collection on time, on budget, without missing observations, and at high quality.
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).
Enumerator training is an extremely important part of the primary data collection, and should be planned in advance.
Back checks are an important tool that allows the research team to verify the quality and validity of survey data.
Quality data is data that is not systematically biased and does not misstate representativeness or coverage. A data quality assurance plan considers everything in data collection that could go wrong ahead of time and makes a plan to preempt these issues.
Sampling is the process of randomly selecting units from a population of interest to represent the characteristics of that population. Sampling in a statistically valid, representative manner is a crucial step in conducting high quality randomized control trials.