Silverwood, Richard J;
Calderwood, Lisa;
Henderson, Morag;
Sakshaug, Joseph W;
Ploubidis, George B;
(2024)
A data-driven approach to understanding non-response and restoring sample representativeness in the UK Next Steps cohort.
Longitudinal and Life Course Studies
10.1332/17579597Y2024D000000010.
Preview |
Text
Silverwood_A data-driven approach to understanding non-response and restoring sample representativeness in the UK Next Steps cohort_VoR.pdf - Published Version Download (952kB) | Preview |
Abstract
Non-response is common in longitudinal surveys, reducing efficiency and introducing the potential for bias. Principled methods, such as multiple imputation, are generally required to obtain unbiased estimates in surveys subject to missingness which is not completely at random. The inclusion of predictors of non-response in such methods, for example as auxiliary variables in multiple imputation, can help improve the plausibility of the missing at random assumption underlying these methods and hence reduce bias. We present a systematic data-driven approach used to identify predictors of non-response at Wave 8 (age 25–26) of Next Steps, a UK national cohort study that follows a sample of 15,770 young people from age 13–14 years. The identified predictors of non-response were across a number of broad categories, including personal characteristics, schooling and behaviour in school, activities and behaviour outside of school, mental health and well-being, socio-economic status, and practicalities around contact and survey completion. We found that including these predictors of non-response as auxiliary variables in multiple imputation analyses allowed us to restore sample representativeness in several different settings, though we acknowledge that this is unlikely to universally be the case. We propose that these variables are considered for inclusion in future analyses using principled methods to explore and attempt to reduce bias due to non-response in Next Steps. Our data-driven approach to this issue could also be used as a model for investigations in other longitudinal studies.
| Type: | Article |
|---|---|
| Title: | A data-driven approach to understanding non-response and restoring sample representativeness in the UK Next Steps cohort |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1332/17579597Y2024D000000010 |
| Publisher version: | http://dx.doi.org/10.1332/17579597y2024d000000010 |
| Language: | English |
| Additional information: | This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International license (http://creativecommons.org/licenses/by-nc/4.0/). |
| Keywords: | Science & Technology, Social Sciences, Life Sciences & Biomedicine, Public, Environmental & Occupational Health, Social Sciences, Interdisciplinary, Social Sciences - Other Topics, cohort studies, missing data, multiple imputation, non-response, sample representativeness, MULTIPLE IMPUTATION, RESIDENTIAL-MOBILITY, SOCIAL-SCIENCE, BIRTH COHORT, BIAS, PARTICIPATION, ATTRITION, EDUCATION, ALCOHOL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10188890 |
Archive Staff Only
 |
View Item |
