Joseph T. Hefner, Kandus C. Linde

Atlas of Human Cranial Macromorphoscopic Traits (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Atlas of Human Cranial Macromorphoscopic Traits synthesizes macromorphoscopic traits and their analysis in an accessible manner, providing detailed descriptions and examples of the various character state manifestations intended for use in classrooms, laboratories, and in the field. The volume begins with an outline of the macromorphoscopic dataset, its history, recent modifications to the historical approach, and recent technological and analytical advances. Additional sections cover Nomenclature, Gross Anatomy, Function, Methodology, Line Drawings, Detailed Definitions, Multiple High-resolution Photographs, and Population Variation Data from the Macromorphoscopic Databank (MaMD).

The volume concludes with a chapter outlining the statistical analysis of macromorphoscopic data and a summary of the computer programs and reference databases available to forensic anthropologists for the analysis of these data.

• Provides detailed descriptions, illustrations and high-resolution images of various character state manifestations of seventeen macromorphoscopic traits
• Applies to both forensic and bioarcheological research
• Written by the foremost expert on macromorphoscopic trait analysis and estimation of ancestry in forensic anthropology

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 356
• Erscheinungstermin: 2. August 2018
• Englisch
• ISBN-13: 9780128143865
• Artikelnr.: 52009864

Autorenporträt

Dr. Hefner is an Assistant Professor of Anthropology at Michigan State University. He received his PhD from the University of Florida, Gainesville. Dr. Hefner is considered one of the world's foremost experts on macromorphoscopic trait analysis and the estimation of ancestry in forensic anthropology. His research interests include: human variation, skeletal biology, forensic anthropology, quantitative methods, human osteology, human anatomy, nonparametric statistics, geometric morphometrics, categorical data analysis, and classification statistics.