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Unit of analysis issues in laboratory-based research

Lookup NU author(s): Professor Dawn Teare



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© Parsons et al. Many studies in the biomedical research literature report analyses that fail to recognise important data dependencies from multilevel or complex experimental designs. Statistical inferences resulting from such analyses are unlikely to be valid and are often potentially highly misleading. Failure to recognise this as a problem is often referred to in the statistical literature as a unit of analysis (UoA) issue. Here, by analysing two example datasets in a simulation study, we demonstrate the impact of UoA issues on study efficiency and estimation bias, and highlight where errors in analysis can occur. We also provide code (written in R) as a resource to help researchers undertake their own statistical analyses.

Publication metadata

Author(s): Parsons NR, Teare MD, Sitch AJ

Publication type: Article

Publication status: Published

Journal: eLife

Year: 2018

Volume: 7

Online publication date: 10/01/2018

Acceptance date: 13/12/2017

Date deposited: 11/11/2019

ISSN (electronic): 2050-084X

Publisher: eLife Sciences Publications Ltd


DOI: 10.7554/eLife.32486

PubMed id: 29319501


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