Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices

Nested data structures create statistical dependence that influences the effective sample size and statistical power of a study. Several methods are available for dealing with nested data, including the summary-statistics approach and multilevel modelling (MLM). Recent publications have heralded MLM...

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Autores principales:	Carolyn Beth McNabb, Kou Murayama
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Hierarchical linear model Mixed model t-test Clustering Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/0bf4faf0e04744bbaca58bde1a357f07
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spelling	oai:doaj.org-article:0bf4faf0e04744bbaca58bde1a357f072021-11-24T04:34:24ZUnnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices2665-945X10.1016/j.crneur.2021.100024https://doaj.org/article/0bf4faf0e04744bbaca58bde1a357f072021-01-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2665945X21000206https://doaj.org/toc/2665-945XNested data structures create statistical dependence that influences the effective sample size and statistical power of a study. Several methods are available for dealing with nested data, including the summary-statistics approach and multilevel modelling (MLM). Recent publications have heralded MLM as the best method for analysing nested data, claiming benefits in power over summary-statistics approaches (e.g., the t-test). However, when cluster size is equal, these approaches are mathematically equivalent. We conducted statistical simulations demonstrating equivalence of MLM and summary-statistics approaches for analysing nested data and provide supportive cases for the utility of the conventional summary-statistics approach in nested experiments. Using statistical simulations, we demonstrate that losses in power in the summary-statistics approach discussed in the previous literature are unsubstantiated. We also show that MLM sometimes suffers from frequent singular fit errors, especially when intraclass correlation is low. There are indeed many situations in which MLM is more appropriate and desirable, but researchers should be aware of the possibility that simpler analysis (i.e., summary-statistics approach) does an equally good or even better job in some situations.Carolyn Beth McNabbKou MurayamaElsevierarticleHierarchical linear modelMixed modelt-testClusteringNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENCurrent Research in Neurobiology, Vol 2, Iss , Pp 100024- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hierarchical linear model Mixed model t-test Clustering Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Hierarchical linear model Mixed model t-test Clustering Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Carolyn Beth McNabb Kou Murayama Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices
description	Nested data structures create statistical dependence that influences the effective sample size and statistical power of a study. Several methods are available for dealing with nested data, including the summary-statistics approach and multilevel modelling (MLM). Recent publications have heralded MLM as the best method for analysing nested data, claiming benefits in power over summary-statistics approaches (e.g., the t-test). However, when cluster size is equal, these approaches are mathematically equivalent. We conducted statistical simulations demonstrating equivalence of MLM and summary-statistics approaches for analysing nested data and provide supportive cases for the utility of the conventional summary-statistics approach in nested experiments. Using statistical simulations, we demonstrate that losses in power in the summary-statistics approach discussed in the previous literature are unsubstantiated. We also show that MLM sometimes suffers from frequent singular fit errors, especially when intraclass correlation is low. There are indeed many situations in which MLM is more appropriate and desirable, but researchers should be aware of the possibility that simpler analysis (i.e., summary-statistics approach) does an equally good or even better job in some situations.
format	article
author	Carolyn Beth McNabb Kou Murayama
author_facet	Carolyn Beth McNabb Kou Murayama
author_sort	Carolyn Beth McNabb
title	Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices
title_short	Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices
title_full	Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices
title_fullStr	Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices
title_full_unstemmed	Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices
title_sort	unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: when a traditional summary-statistics approach suffices
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0bf4faf0e04744bbaca58bde1a357f07
work_keys_str_mv	AT carolynbethmcnabb unnecessaryrelianceonmultilevelmodellingtoanalysenesteddatainneurosciencewhenatraditionalsummarystatisticsapproachsuffices AT koumurayama unnecessaryrelianceonmultilevelmodellingtoanalysenesteddatainneurosciencewhenatraditionalsummarystatisticsapproachsuffices
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Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices

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