Computationally intensive multivariate statistics and relative frequency distributions in archaeology (with an application to the Early Epipaleolithic of the Levant)
(2004) In Journal of Archaeological Science 31(12). p.1643-1658- Abstract
- Archaeologists seek to analyze patterns of similarity and difference among diverse kinds of assemblages that (1) vary in the number of specimens and (2) have been characterized by standard multi-category frequency distributions. Recent developments in computer simulation methods offer marked improvements in our ability to test statistical hypotheses about variation in relative taxonomic or typological abundance data, drawn from assemblages of variable sizes and diverse archaeological contexts (American Antiquity 66 (2001) 715; Journal of Archaeological Science 30 (2003) 837). In this article we extend the highly flexible and powerful computationally intensive framework for statistical analysis to the multivariate family of methods of... (More)
- Archaeologists seek to analyze patterns of similarity and difference among diverse kinds of assemblages that (1) vary in the number of specimens and (2) have been characterized by standard multi-category frequency distributions. Recent developments in computer simulation methods offer marked improvements in our ability to test statistical hypotheses about variation in relative taxonomic or typological abundance data, drawn from assemblages of variable sizes and diverse archaeological contexts (American Antiquity 66 (2001) 715; Journal of Archaeological Science 30 (2003) 837). In this article we extend the highly flexible and powerful computationally intensive framework for statistical analysis to the multivariate family of methods of analysis of variation (MANOVA) and non-hierarchical cluster analysis. We treat the relative type-frequency distribution as a multivariate quantitative description of the archaeological assemblage. We then introduce two simulation-based computer applications for analyzing variability between groups of assemblages. We utilize the multivariate applications in a case study; we evaluate how standard microlith typological classifications perform in capturing information about technological and formal variability among Early Epipaleolithic microlith assemblages from the Southern Levant. Computationally intensive, simulation-based statistical techniques allow the researcher to custom-tailor the measure of statistical variation and the model of random archaeological record formation relevant to the given problem. We suggest that with simulation-based approaches, the costs of computer programming and processing are far outweighed by the potential for explaining quantifiable variability in material traces of human activity in the past. (C) 2004 Elsevier Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/262410
- author
- Stutz, Aaron LU and Estabrook, GF
- publishing date
- 2004
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- multivariate, statistical analysis, relative frequencies, computationally intensive
- in
- Journal of Archaeological Science
- volume
- 31
- issue
- 12
- pages
- 1643 - 1658
- publisher
- Academic Press
- external identifiers
-
- wos:000224838400001
- scopus:5144223563
- ISSN
- 1095-9238
- DOI
- 10.1016/j.jas.2004.04.005
- language
- English
- LU publication?
- no
- id
- 2eb9e38c-f020-4c18-89a5-4ef263262cf8 (old id 262410)
- date added to LUP
- 2016-04-01 12:13:32
- date last changed
- 2022-03-13 07:01:27
@article{2eb9e38c-f020-4c18-89a5-4ef263262cf8,
abstract = {{Archaeologists seek to analyze patterns of similarity and difference among diverse kinds of assemblages that (1) vary in the number of specimens and (2) have been characterized by standard multi-category frequency distributions. Recent developments in computer simulation methods offer marked improvements in our ability to test statistical hypotheses about variation in relative taxonomic or typological abundance data, drawn from assemblages of variable sizes and diverse archaeological contexts (American Antiquity 66 (2001) 715; Journal of Archaeological Science 30 (2003) 837). In this article we extend the highly flexible and powerful computationally intensive framework for statistical analysis to the multivariate family of methods of analysis of variation (MANOVA) and non-hierarchical cluster analysis. We treat the relative type-frequency distribution as a multivariate quantitative description of the archaeological assemblage. We then introduce two simulation-based computer applications for analyzing variability between groups of assemblages. We utilize the multivariate applications in a case study; we evaluate how standard microlith typological classifications perform in capturing information about technological and formal variability among Early Epipaleolithic microlith assemblages from the Southern Levant. Computationally intensive, simulation-based statistical techniques allow the researcher to custom-tailor the measure of statistical variation and the model of random archaeological record formation relevant to the given problem. We suggest that with simulation-based approaches, the costs of computer programming and processing are far outweighed by the potential for explaining quantifiable variability in material traces of human activity in the past. (C) 2004 Elsevier Ltd. All rights reserved.}},
author = {{Stutz, Aaron and Estabrook, GF}},
issn = {{1095-9238}},
keywords = {{multivariate; statistical analysis; relative frequencies; computationally intensive}},
language = {{eng}},
number = {{12}},
pages = {{1643--1658}},
publisher = {{Academic Press}},
series = {{Journal of Archaeological Science}},
title = {{Computationally intensive multivariate statistics and relative frequency distributions in archaeology (with an application to the Early Epipaleolithic of the Levant)}},
url = {{http://dx.doi.org/10.1016/j.jas.2004.04.005}},
doi = {{10.1016/j.jas.2004.04.005}},
volume = {{31}},
year = {{2004}},
}