ArticleBehavior research methods2018
Biases in multilevel analyses caused by cluster-specific fixed-effects imputation.
Article in Behavior research methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Effectiveness and cost-effectiveness of The Daily Mile on childhood weight outcomes and wellbeing: a cluster randomised controlled trial.International journal of obesity (2005) · 2020Trial
- Estimating multivariate longitudinal trajectories using mixed-effects models with crossed random effects.Behavior research methods · 2026Article
- Adapting tree-based multiple imputation methods for multilevel data? A simulation study.Behavior research methods · 2026Article
- Complete sources of cluster variation on the risk of under-five malaria in Uganda: a multilevel-weighted mixed effects logistic regression model approach.Malaria journal · 2023Article
- Evaluation of approaches for accommodating interactions and non-linear terms in multiple imputation of incomplete three-level data.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Multiple imputation approaches for handling incomplete three-level data with time-varying cluster-memberships.Statistics in medicine · 2022Article
- How to Improve Healthcare for Patients with Multimorbidity and Polypharmacy in Primary Care: A Pragmatic Cluster-Randomized Clinical Trial of the MULTIPAP Intervention.Journal of personalized medicine · 2022Article
- Evaluation of approaches for multiple imputation of three-level data.BMC medical research methodology · 2020Article
- A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children's Physical Activity.International journal of environmental research and public health · 2019Article
Corrections and comments
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3 authors.
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Abstract
When datasets are affected by nonresponse, imputation of the missing values is a viable solution. However, most imputation routines implemented in commonly used statistical software packages do not accommodate multilevel models that are popular in education research and other settings involving clustering of units. A common strategy to take the hierarchical structure of the data into account is to include cluster-specific fixed effects in the imputation model. Still, this ad hoc approach has never been compared analytically to the congenial multilevel imputation in a random slopes setting. In this paper, we evaluate the impact of the cluster-specific fixed-effects imputation model on multilevel inference. We show analytically that the cluster-specific fixed-effects imputation strategy will generally bias inferences obtained from random coefficient models. The bias of random-effects variances and global fixed-effects confidence intervals depends on the cluster size, the relation of within- and between-cluster variance, and the missing data mechanism. We illustrate the negative implications of cluster-specific fixed-effects imputation using simulation studies and an application based on data from the National Educational Panel Study (NEPS) in Germany.
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