Misuse of analysis of variance in African biomedical journals: a call for more vigilance

Abubakar, Bilyaminu; Uthman, Yaaqub Abiodun; Jatau, Abubakar Ibrahim; Danbatta, Abubakar; Nuhu, Hafsat Nasidi; Mohammed, Mustapha

doi:10.1186/s42269-022-00924-8

Cited by 2 publications

(1 citation statement)

References 23 publications

(24 reference statements)

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“…Several documents highlight the misuse of ANOVA in two-way designs due to heterogeneous variance (Rowell & Walters, 2006) highlighting defective data (Pearce, 2006). Defective data includes the nonspeci cation of the type of ANOVA (Abubakar et al, 2022), confusion in the choice of the type of effects ( xed or random) (Bennington & Thayne, 1994), assumption too quickly of the weak argument that not rejecting the null hypothesis implies a strong conclusion (Rong, 2000), inappropriate use of the terms associated with the error in the construction of the statistical F or the type of sums of squares (Li & Lomax, 2011). Such defective data even includes the belief, supported in recognized literature that only a couple of assumptions are necessary to make use of the technique (Acutis et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

The effect of spatial lag on modeling geomatic covariates using analysis of variance

Contreras,

Lozada,

Sotomayor

et al. 2023

Preprint

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In recent years, statistical methods involving spatial considerations have been developed, for example, those incorporating data with some type of georeferencing. The descriptive part of geographic information systems currently provides many visualization and analytic tools; however, the latter is still quite limited. In this sense, research of a spatial nature is seen as combining non-spatial statistical methods for inferential treatment that can certainly invalidate the excellent capture work with advanced tools such as those observed every day in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case it is associated with the lag or spatial overlap. The methodology is simple to adopt in other experimental designs with the simple consideration of the design matrix and its reparameterization and the choice of the appropriate weight matrix. This will allow users to reconsider the traditional method of analysis and incorporate some methodology to support spatial dependency structures.

show abstract

Section: Introductionmentioning

confidence: 99%

The effect of spatial lag on modeling geomatic covariates using analysis of variance

Contreras,

Lozada,

Sotomayor

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

The effect of spatial lag on modeling geomatic covariates using analysis of variance

Aquiles E.,

Darlley S.,

Nair J.

et al. 2024

Appl Geomat

View full text Add to dashboard Cite

In recent years, statistical methods have been developed that include spatial considerations, for example, those that incorporate data with georeferencing. The descriptive part of geographical information systems currently provides many visualization and analysis tools; however, in terms of analysis, these systems are still quite limited, therefore, ignorance of these limitations may result in data with spatial effects being treated with conventional statistical methods for non-spatial use, which can certainly invalidate the excellent work of data capture with advanced tools such as those that are used daily in the geomatic context. This prompted the current document, drawing attention to how geomatic information analyzed with statistical methods that imply independence in modeled observations can be invalid. The Moran index is compared with a proposal for a spatial lag coefficient in the context of experimental design so that users of variance analysis do not apply this well-known procedure in a ritualistic way, perhaps revising some assumptions and perhaps ignoring more important ones. The distortion of the p value generated from the analysis of variance is clear in the presence of spatial dependence. In this case, it is associated with the lag or spatial overlap. The methodology is easy to apply in other designs with the development of the design matrix, its reparameterization and the choice of the respective weight matrix. This may cause users to reconsider the traditional method of analysis and incorporate some appropriate analysis methodology to address spatial effects present in data or in outputs from the modeling process.

show abstract

Misuse of analysis of variance in African biomedical journals: a call for more vigilance

Cited by 2 publications

References 23 publications

The effect of spatial lag on modeling geomatic covariates using analysis of variance

The effect of spatial lag on modeling geomatic covariates using analysis of variance

The effect of spatial lag on modeling geomatic covariates using analysis of variance

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