Multilevel and mixed-effects models: Enhancing analytical rigour in public health research
- 1School of Public Health, College of Medicine and Health Sciences, Hawassa University, Hawassa, Ethiopia
Abstract
Public health research often involves data where individuals are nested within groups, such as households, schools, or administrative regions. Standard regression methods typically assume independence among observations; when this assumption is violated, biased standard errors and misleading conclusions can result. Multilevel and mixed‑effects models are designed to account for clustering by partitioning variance into within‑group and between‑group components. This allows researchers to examine how group‑level factors modify individual‑level associations. However, these models are one important option among several valid approaches for clustered data, including generalized estimating equations, cluster‑robust standard errors, and fixed‑effects methods. This commentary argues that multilevel and mixed‑effects models are especially useful when researchers aim to quantify contextual variation, estimate cluster‑level random effects, or examine cross‑level interactions; nevertheless, model choice should be guided by the research question, data structure, and inferential goal rather than by the assumption that a single method defines evidence quality.
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BibTeX
@article{debessa2026multilevel,
title = {{Multilevel and mixed-effects models: Enhancing analytical rigour in public health research}},
author = {Kare Chawicha Debessa},
journal = {Journal of Interventional Epidemiology and Public Health},
year = {2026},
month = jul,
doi = {10.37432/jieph-d-26-00137},
url = {https://doi.org/10.37432/jieph-d-26-00137},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multilevel and mixed-effects models: Enhancing analytical rigour in public health research
AU - Kare Chawicha Debessa
PY - 2026
DA - 2026/07/01
JO - Journal of Interventional Epidemiology and Public Health
DO - 10.37432/jieph-d-26-00137
UR - https://doi.org/10.37432/jieph-d-26-00137
AB - Public health research often involves data where individuals are nested within groups, such as households, schools, or administrative regions. Standard regression methods typically assume independence among observations; when this assumption is violated, biased standard errors and misleading conclusions can result. Multilevel and mixed‑effects models are designed to account for clustering by partitioning variance into within‑group and between‑group components. This allows researchers to examine how group‑level factors modify individual‑level associations. However, these models are one important option among several valid approaches for clustered data, including generalized estimating equations, cluster‑robust standard errors, and fixed‑effects methods. This commentary argues that multilevel and mixed‑effects models are especially useful when researchers aim to quantify contextual variation, estimate cluster‑level random effects, or examine cross‑level interactions; nevertheless, model choice should be guided by the research question, data structure, and inferential goal rather than by the assumption that a single method defines evidence quality.
SN - 2664-2824
ER -