The effect of events between waves on panel attrition

Panel surveys suffer from attrition. Most panel studies use propensity models or weighting class approaches to correct for non-random dropout. These models draw on variables measured in a previous wave or from paradata of the study. While it is plausible that they affect contactability and cooperati...

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Main Authors: Mark Trappmann, Tobias Gramlich, Alexander Mosthaf
Format: Article
Language:English
Published: European Survey Research Association 2015-04-01
Series:Survey Research Methods
Subjects:
Online Access:https://ojs.ub.uni-konstanz.de/srm/article/view/5849
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spelling doaj-bb0682bef0664fa4bc5b715e11258e532020-11-24T22:54:26ZengEuropean Survey Research AssociationSurvey Research Methods1864-33611864-33612015-04-0191314310.18148/srm/2015.v9i1.58495697The effect of events between waves on panel attritionMark Trappmann0Tobias GramlichAlexander MosthafInstitute for Employment Research (IAB) Otto-Friedrich-Universität BambergPanel surveys suffer from attrition. Most panel studies use propensity models or weighting class approaches to correct for non-random dropout. These models draw on variables measured in a previous wave or from paradata of the study. While it is plausible that they affect contactability and cooperativeness, panel studies usually cannot assess the impact of events between waves on attrition. The amount of change in the population could be seriously underestimated if such events had an effect on participation in subsequent waves. The panel study PASS is a novel dataset for labour market and poverty research. In PASS, survey data on (un)employment histories, income and education of participants are linked to corresponding data from respondents' administrative records. Thus, change can be observed for attritors as well as for continued participants. These data are used to show that change in household composition, employment status or receipt of benefits has an influence on contact and cooperation rates in the following wave. A large part of the effect is due to lower contactability of households who moved. Nevertheless, this effect can lead to biased estimates for the amount of change. After applying the survey’s longitudinal weights this bias is reduced, but not entirely eliminated.https://ojs.ub.uni-konstanz.de/srm/article/view/5849panel surveyattritionnonresponseweighting
collection DOAJ
language English
format Article
sources DOAJ
author Mark Trappmann
Tobias Gramlich
Alexander Mosthaf
spellingShingle Mark Trappmann
Tobias Gramlich
Alexander Mosthaf
The effect of events between waves on panel attrition
Survey Research Methods
panel survey
attrition
nonresponse
weighting
author_facet Mark Trappmann
Tobias Gramlich
Alexander Mosthaf
author_sort Mark Trappmann
title The effect of events between waves on panel attrition
title_short The effect of events between waves on panel attrition
title_full The effect of events between waves on panel attrition
title_fullStr The effect of events between waves on panel attrition
title_full_unstemmed The effect of events between waves on panel attrition
title_sort effect of events between waves on panel attrition
publisher European Survey Research Association
series Survey Research Methods
issn 1864-3361
1864-3361
publishDate 2015-04-01
description Panel surveys suffer from attrition. Most panel studies use propensity models or weighting class approaches to correct for non-random dropout. These models draw on variables measured in a previous wave or from paradata of the study. While it is plausible that they affect contactability and cooperativeness, panel studies usually cannot assess the impact of events between waves on attrition. The amount of change in the population could be seriously underestimated if such events had an effect on participation in subsequent waves. The panel study PASS is a novel dataset for labour market and poverty research. In PASS, survey data on (un)employment histories, income and education of participants are linked to corresponding data from respondents' administrative records. Thus, change can be observed for attritors as well as for continued participants. These data are used to show that change in household composition, employment status or receipt of benefits has an influence on contact and cooperation rates in the following wave. A large part of the effect is due to lower contactability of households who moved. Nevertheless, this effect can lead to biased estimates for the amount of change. After applying the survey’s longitudinal weights this bias is reduced, but not entirely eliminated.
topic panel survey
attrition
nonresponse
weighting
url https://ojs.ub.uni-konstanz.de/srm/article/view/5849
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