Biological Anthropology of the Human Skeleton 2018
DOI: 10.1002/9781119151647.ch17
|View full text |Cite
|
Sign up to set email alerts
|

Traditional Morphometrics and Biological Distance

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
4
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 7 publications
(4 citation statements)
references
References 120 publications
0
4
0
Order By: Relevance
“…This study presents a hitherto unexplored craniometric dataset. This dataset, included as an online supplement, will be useful to conduct comparative craniometric studies on global populations (Howells, 1973;Pietrusewsky, 2019). Culturally or biologically affiliated descendants are increasingly showing an interest in scientific studies of their ancestors represented in skeletal collections (Turnbull, 2016;Roberts et al, 2018;Wright et al, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…This study presents a hitherto unexplored craniometric dataset. This dataset, included as an online supplement, will be useful to conduct comparative craniometric studies on global populations (Howells, 1973;Pietrusewsky, 2019). Culturally or biologically affiliated descendants are increasingly showing an interest in scientific studies of their ancestors represented in skeletal collections (Turnbull, 2016;Roberts et al, 2018;Wright et al, 2018).…”
Section: Discussionmentioning
confidence: 99%
“…A canonical variates analysis (CVA: a linear combination of predictor variables that summarize among-population variation) was conducted using the newly calculated shape variables [ 82 ]. Among-group differentiation was measured using Mahalanobis squared distances, which is a similarity measure and a function of group means and the pooled variance–covariance matrix [ 82 ]. A hierarchical (or agglomerative) cluster analysis using average linkage was conducted on the Mahalanobis squared distances to examine group similarity [ 70 ].…”
Section: Methodsmentioning
confidence: 99%
“…Average linkage hierarchical (or agglomerative) clustering was conducted using the generalized distance matrix to examine group similarity [33,34]. The process begins with each population sample in a single cluster, then in each successive iteration, it merges the closest pair of clusters until all the data is in one cluster.…”
Section: Hierarchical Clusteringmentioning
confidence: 99%