Determinación sexual en cráneos de sitios arqueológicos del humedal del paraná inferior: Una aproximación desde análisis cualitativos y cuantitativos

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Abstract

La determinación del sexo en restos óseos humanos constituye en bioarqueología un tema de especial interés cuando se efectúan interpretaciones de índole conductual y cultural en poblaciones del pasado. En el presente trabajo se propone discutir la aplicación de metodologías cualitativas y cuantitativas a una muestra de 110 cráneos de individuos adultos de ambos sexos provenientes de sitios arqueológicos del Humedal del Paraná inferior (Argentina) con el fin de obtener una discriminación sexual adecuada para la muestra estudiada y evaluar la eficacia de dichas metodologías. Las variables cualitativas relevadas incluyen grados de dimorfismo sexual registradas en la glabela, en el margen supraorbital, en el proceso mastoideo, en la cresta occipital y en la eminencia mentoniana según un sistema ordinal de expresión (scoring). Los análisis métricos se llevaron a cabo en el proceso mastoideo a partir de las siguientes mediciones: ancho máximo, ancho mínimo, ancho transversal, ancho horizontal, longitud porion-mastoidal, longitud asterion-mastoidal, longitud porion-mastoidal vertical y cálculo de la superficie del área. Todas las variables presentaron diferencias estadísticamente significativas y altos porcentajes de discriminación. Posteriormente se elaboró una función discriminante a partir de las variables métricas que mejor discriminaban entre sexos. Si bien se logró una buena diferenciación sexual tanto a través de los análisis cualitativos como de los cuantitativos se recomienda su uso en forma conjunta para mejorar la precisión en los resultados.

Translated abstract

Sex determination on human bone remains in bioarchaeology constitutes a special issue to perform interpretations on behavioral and cultural aspects of past societies. The aim of the present work is to discuss the application of qualitative and quantitative methodologies on a sample of 110 skulls of adult individuals of both sexes from archaeological sites of the Paraná inferior Wetland (Argentina), with the purpose to obtain an adequate sexual discrimination for the present sample and to evaluate the efficiency of those methodologies. The qualitative variables recorded include degrees of sexual dimorphism in the glabella, supraorbital margin, mastoid process, nuchal crest and in the mental eminence according to an ordinal system of expression (scoring). The metric analysis was carried out on the mastoid process using the following measures: maximum breath, minimum breath, transversal breath, horizontal breath, porion- mastoidale length, asterion-mastoidale length, vertical porion-mastoidale length and area surface calculation. All variables presented statistically significant differences and high discrimination percentages. Subsequently a discriminant function was elaborated using those metric variables that better discriminated between sexes. Even though a good sexual discrimination was achieved through qualitative and quantitative analyses, the results suggest that both should be implemented to increase the precision in the results.

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Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, discourage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayesian multiple imputation (MI). Newer developments are discussed, including some for dealing with missing data that are not MAR. Although not yet in the mainstream, these procedures may eventually extend the ML and MI methods that currently represent the state of the art.

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J Schafer, M. Olsen (1998)

Analyses of multivariate data are frequently hampered by missing values. Until recently, the only missing-data methods available to most data analysts have been relatively ad1 hoc practices such as listwise deletion. Recent dramatic advances in theoretical and computational statistics, however, have produced anew generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m > 1 plausible values. The rn versions of the complete data are analyzed by standard complete-data methods, and the results are combined using simple rules to yield estimates, standard errors, and p-values that formally incorporate missing-data uncertainty. New computational algorithms and software described in a recent book (Schafer, 1997a) allow us to create proper multiple imputations in complex multivariate settings. This article reviews the key ideas of multiple imputation, discusses the software programs currently available, and demonstrates their use on data from the Adolescent Alcohol Prevention Trial (Hansen & Graham, 199 I).