Power Estimation in Multivariate Analysis of Variance

Allaire, Jean François; Chartier, Sylvain

doi:10.20982/tqmp.03.2.p070

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…For the purpose of this study a total of 473 cut marks were produced and analysed. Such a large sample size was produced considering the statistical power required to truly evaluate cut mark variability (Chartier and Allarie, 2007;Faul et al, 2007;Reinhart, 2015;Courtenay et al, 2018a). Cut marks were produced on different anatomical parts of two different taxa using a metal knife; one prime adult bovid and one prime adult suid were selected for the study (Fig.…”

Section: Methods and Samplesmentioning

confidence: 99%

Application of geometric morphometrics to the analysis of cut mark morphology on different bones of differently sized animals. Does size really matter?

Maté-González

Courtenay

Aramendi

et al. 2019

Quaternary International

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Section: Methods and Samplesmentioning

confidence: 99%

Application of geometric morphometrics to the analysis of cut mark morphology on different bones of differently sized animals. Does size really matter?

Maté-González

Courtenay

Aramendi

et al. 2019

Quaternary International

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“…Their current sample size (17 slices and 27 cuts) shows a power of 0.16 to detect small effect sizes (0.3) and of 0.36 to detect moderate effect size (0.5); that is, the probability of having Type II errors in their sample range from 0.84 to 0.64. Although Otárola-Castillo et al (2018) are not performing comparative metric analyses but classification tests, the small sample they used can also affect the classification rates they derived and the multivariate analysis of their data, especially the MANOVA results ( Chartier & Allarie, 2007 ; Faul et al, in press ).…”

Section: Discussionmentioning

confidence: 99%

Testing accuracy in 2D and 3D geometric morphometric methods for cut mark identification and classification

Courtenay

Maté-González

Aramendi

et al. 2018

PeerJ

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The analysis of bone surface modifications (BSMs) is a prominent part of paleoanthropological studies, namely taphonomic research. Behavioral interpretations of the fossil record hinge strongly upon correct assessment of BSMs. With the significant impact of microscopic analysis to the study of BSMs, multiple authors have discussed the reliability of these technological improvements for gaining resolution in BSM discrimination. While a certain optimism is present, some important questions are ignored and others overemphasized without appropriate empirical support. This specifically affects the study of cut marks. A diversity of geometric morphometric approaches applied to the study of cut marks have resulted in the coexistence (and competition) of different 2D and 3D methods. The present work builds upon the foundation of experiments presented by Maté-González et al. (2015), Courtenay et al. (2017) and Otárola-Castillo et al. (2018) to contrast for the first time 2D and 3D methods in their resolution of cut mark interpretation and classification. The results presented here show that both approaches are equally valid and that the use of sophisticated 3D methods do not contribute to an improvement in accuracy.

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“…confidence intervals, power, standardized weights, etc.). Concerning power, Chartier & Allaire (2008) present this concept as applied to the multivariate scheme. Although some interesting properties of special cases (e.g.…”

Section: Discussionmentioning

confidence: 99%