Evaluating data quality on the measurement of change using dependent interviewing


Dependent interviewing (DI) uses respondent data from earlier waves in panel surveys to improve the data quality of change estimates. Apart from a positive effect on data quality through reducing overestimations of change, DI could also affect data quality negatively when it leads to satisficing and an overestimation of stability between waves. In this article, we experimentally test two frequently used DI designs under different levels of measurement error. Our data consist of income reports from a four-wave panel survey conducted in the Netherlands. The effects of our experiment on data quality are modeled with a quasi-simplex structure to enable the decomposition of variances into measurement errors and true change. Our main conclusion is that there is some risk of a negative effect on data quality for proactive DI but not for reactive DI.

Field Methods 26, 172-189