Which field data collection practices minimize bias and errors?

• A. Use standardized forms, train collectors, collect data promptly, verify with records when possible, and minimize missing data through checks
• B. Collect data solely by quick interviews without forms
• C. Rely on secondary data sources only
• D. Delay data collection until after the outbreak ends

Answer: (A)

The main idea here is that reducing bias and errors in field data hinges on consistency, proper training, timely collection, external verification, and built-in checks for completeness. Using standardized forms ensures every data collector asks and records the same questions in the same way, which minimizes measurement variation and misinterpretation. Training supports uniform interviewing techniques and a clear understanding of definitions, so different collectors collect data in a comparable manner. Collecting data promptly captures information while it’s fresh, reducing recall bias and changes that can creep in if data are delayed. Verifying information against records provides an external check, helping catch and correct inaccuracies before they propagate. Implementing checks to minimize missing data keeps the dataset complete and reduces the need for dubious imputations or biased assumptions.

In contrast, relying on quick interviews without structured forms invites inconsistent questions and poor comparability. Depending only on secondary data sources can introduce outdated or incomplete information and limits the ability to verify accuracy. Delaying data collection until after an outbreak ends invites memory decay and loss of important details, increasing recall bias and data gaps.