Modelling mode effects for a panel survey in transition


Biemer, P. P.; Harris, K. M.; Liao, D.; Burke, B. J.; & Halpern, C. T. (2021). Modelling mode effects for a panel survey in transition. In Measurement Error in Longitudinal Data. (pp. 63-88). Oxford University Press.


Funding reductions combined with increasing data-collection costs required that Wave V of the USA’s National Longitudinal Study of Adolescent to Adult Health (Add Health) abandon its traditional approach of in-person interviewing and adopt a more cost-effective method. This approach used the mail/web mode in Phase 1 of data collection and in-person interviewing for a random sample of nonrespondents in Phase 2. In addition, to facilitate the comparison of modes, a small random subsample served as the control and received the traditional in-person interview. We show that concerns about reduced data quality as a result of the redesign effort were unfounded based on findings from an analysis of the survey data. In several important respects, the new two-phase, mixed-mode design outperformed the traditional design with greater measurement accuracy, improved weighting adjustments for mitigating the risk of nonresponse bias, reduced residual (or post-adjustment) nonresponse bias, and substantially reduced total-mean-squared error of the estimates. This good news was largely unexpected based upon the preponderance of literature suggesting data quality could be adversely affected by the transition to a mixed mode. The bad news is that the transition comes with a high risk of mode effects for comparing Wave V and prior wave estimates. Analytical results suggest that significant differences can occur in longitudinal change estimates about 60 % of the time purely as an artifact of the redesign. This begs the question: how, then, should a data analyst interpret significant findings in a longitudinal analysis in the presence of mode effects? This chapter presents the analytical results and attempts to address this question. © Oxford University Press 2021.



Add health


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Reference Type

Book Chapter

Book Title

Measurement Error in Longitudinal Data


Biemer, P. P.
Harris, K. M.
Liao, D.
Burke, B. J.
Halpern, C. T.

Year Published





Oxford University Press


9780198859987 (ISBN)



Reference ID