Dependent interviewing: a remedy or a curse for measurement error in surveys?

Abstract

Longitudinal surveys often rely on dependent interviewing (DI) to lower the levels of random measurement error in survey data and reduce the incidence of spurious change. DI refers to a data collection technique that incorporates information from prior interview rounds into subsequent waves. While this method is considered an effective remedy for random measurement error, it can also introduce more systematic errors, in particular when respondents are first reminded of their previously provided answer and then asked about their current status. The aim of this paper is to assess the impact of DI on measurement error in employment mobility. We take advantage of a unique experimental situation that was created by the roll-out of dependent interviewing in the Dutch Labour Force Survey (LFS). We apply Hidden Markov Modeling (HMM) to linked LFS and Employment Register (ER) data that cover a period before and after dependent interviewing was abolished, which in turn enables the modeling of systematic errors in the LFS data. Our results indicate that DI lowered the probability of obtaining random measurement error but had no significant effect on the systematic component of the error. The lack of a significant effect might be partially due to the fact that the probability of repeating the same error was extremely high at baseline (i.e when using standard, independent interviewing); therefore the use of DI could not increase this probability any further.