Reducing bias due to missing values of the response variable by joint modeling with an auxiliary variable

Alfonso Miranda, Sophia Rabe-Hesketh and John W. McDonald ()
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John W. McDonald: Department of Quantitative Social Science, Institute of Education, University of London. 20 Bedford Way, London WC1H 0AL, UK.

No 12-05, DoQSS Working Papers from Quantitative Social Science - UCL Social Research Institute, University College London

Abstract: In this paper, we consider the problem of missing values of a continuous response variable that cannot be assumed to be missing at random. The example considered here is an analysis of pupil's subjective engagement at school using longitudinal survey data, where the engagement score from wave 3 of the survey is missing due to a combination of attrition and item non-response. If less engaged students are more likely to drop out and less likely to respond to questions regarding their engagement, then missingness is not ignorable and can lead to inconsistent estimates. We suggest alleviating this problem by modelling the response variable jointly with an auxiliary variable that is correlated with the response variable and not subject to non-response. Such auxiliary variables can be found in administrative data, in our example, the National Pupil Database containing test scores from national achievement tests. We estimate a joint model for engagement and achievement to reduce the bias due to missing values of engagement. A Monte Carlo study is performed to compare our proposed multivariate response approach with alternative approaches such as the Heckman selection model and inverse probability of selection weighting.

Keywords: Auxiliary variable; joint model; multivariate regression; not missing at random; sample selection bias; seemingly-unrelated regressions; selection model; SUR (search for similar items in EconPapers)
JEL-codes: C13 C33 I21 (search for similar items in EconPapers)
Date: 2012-06-29
New Economics Papers: this item is included in nep-ecm
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