The Effect of Rest Period on Response Likelihood

February 19, 2016, 13:45 h

Mannheim, Conference room B2,1

Jennifer Sinibaldi


Given the multitude of survey invitations issued to an individual, survey researchers worry about the impact of repeated requests on cooperation with their specific survey. This analysis uses the extensive survey cooperation history of one government agency, Statistics Iceland, and complementary registry data to identify individuals selected into more than one sample for all Statistics Iceland general population household surveys over a twelve year period. Examining these 11,500 repeatedly sampled individuals, we model whether the decision to participate in the second survey depends on the length of time between survey invitations (i.e., rest period). The results show a linear positive effect between the length of the rest period and participation in the second survey request but this is explained by a combination of demographic characteristics and survey indicators. A persistent effect in the model is the large, positive correlation between cooperation with the first survey and cooperation with the second. Also, being sampled first for a burdensome survey increases the likelihood to participate in a subsequent survey which introduces an interesting twist to understanding how multiple survey invitations affect one another over time. While the objective of the study was to quantify the effect of rest period, the results more so highlight the importance of better understanding repeat respondents to improve protocols for gaining cooperation and address potential nonresponse bias. The analysis has broad applicability for not only individual survey projects but government and academic organizations that release multiple independent surveys, particularly those implementing a rest period.

About the speaker

Jennifer Sinibaldi is a Postdoctoral Fellow and the Coordinator of the Online Degree Programs at the Joint Program in Survey Methodology (JPSM) at the University of Maryland, and the International Program in Survey and Data Science (IPSDS), formed through a partnership between JPSM and the University of Mannheim. Jennifer has her doctorate from LMU in Munich, Germany, and her Master’s from the Survey Methodology Program at the University of Michigan. Between degrees, Jennifer worked at IAB in Nuremberg, Germany, and NatCen in London, England. She also coordinated the International Workshop on Household Nonresponse for five years, until 2015. Her published research addresses questions about the quality and use of paradata, especially interviewer observations, for nonresponse applications.