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The power of online panel paradata to predict unit nonresponse and voluntary attrition in a longitudinal design


Kocar, S and Biddle, N, The power of online panel paradata to predict unit nonresponse and voluntary attrition in a longitudinal design, Quality and Quantity pp. 1-24. ISSN 0033-5177 (2022) [Refereed Article]

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2022 The Author(s) This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License, ( which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

DOI: doi:10.1007/s11135-022-01385-x


The objective of this study is to identify factors affecting participation rates, i.e., nonresponse and voluntary attrition rates, and their predictive power in a probability-based online panel. Participation for this panel had already been investigated in the literature according to the socio-demographic and socio-psychological characteristics of respondents and different types of paradata, such as device type or questionnaire navigation, had also been explored. In this study, the predictive power of online panel participation paradata was instead evaluated, which was expected (at least in theory) to offer even more complex insight into respondents' behavior over time. This kind of paradata would also enable the derivation of longitudinal variables measuring respondents' panel activity, such as survey outcome rates and consecutive waves with a particular survey outcome prior to a wave (e.g., response, noncontact, refusal), and could also be used in models controlling for unobserved heterogeneity. Using the Life in Australia participation data for all recruited members for the first 30 waves, multiple linear, binary logistic and panel random-effect logit regression analyses were carried out to assess socio-demographic and online panel paradata predictors of nonresponse and attrition that were available and contributed to the accuracy of prediction and the best statistical modeling. The proposed approach with the derived paradata predictors and random-effect logistic regression proved to be reasonably accurate for predicting nonresponse-with just 15 waves of online panel paradata (even without sociodemographics) and logit random-effect modeling almost four out of five nonrespondents could be correctly identified in the subsequent wave.

Item Details

Item Type:Refereed Article
Keywords:online panels; paradata; prediction; unit nonresponse; attrition;panel voluntary attrition; random effect logit mode; prediction modeling
Research Division:Human Society
Research Group:Sociology
Research Field:Sociological methodology and research methods
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in human society
UTAS Author:Kocar, S (Dr Sebastian Kocar)
ID Code:153123
Year Published:2022
Deposited By:CALE Research Institute
Deposited On:2022-09-07
Last Modified:2022-10-26
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