Standard quality control procedures for face-to-face surveys use a set of techniques to measure data quality including resurveying respondents on a subset of questions (“backchecking”), accompanying enumerators during the start of the survey to target retraining, and a set of automated data checks. The pivot to remote survey modes made some of these quality control processes impossible to implement.

 

IPA Colombia piloted a data quality review system meant to improve retention rates and response quality during a high-frequency computer-assisted telephone interview (CATI) that lasted eight days. Due to concerns about low response rate in the follow-up, the project team elected to not backcheck surveys, where researchers resurvey a random subset of respondents to estimate data quality measures. Instead, the project team leveraged audio metadata and double entry from audio recordings to identify potential errors and areas of improvement for interviewer retraining.