MODULE DESCRIPTION 2018-19

SPRING SEMESTER 15 CREDITS
AAP6144 QUANTITATIVE METHODS IN ANTHROPOLOGY AND ARCHAEOLOGY
CO-ORDINATOR: KEVIN KUYKENDALL
OTHER TUTORS: ELIZABETH CRAIG-ATKINS

MODULE OUTLINE

This module introduces learners to current research methods for the analysis of archaeological and anthropological data using advanded statistical and computational methods. The module includes lectures and practical classes which explore a series of examples of the application of statistics and numerical methods to quantitative problems in the archaeological sciences including biological anthropology, palaeoanthropology and environmental archaeology.


BROAD ACADEMIC AIMS AND PRINCIPLES OF THE UNIT

This unit aims to:

  • Familiarise students with some of the principal quantitative techniques currently used in data analysis in the archaeological sciences;
  • Familiarise students with text and online sources for learning statistical methods, as well as open-source and published resources for relevant data in biological anthropology and related fields;
  • Provide hands-on experience for students in the use of statistical computer applications such as Excel and SPSS;
  • Foster an appreciation of how statistical analysis can be used, interpreted and critiqued in academic research.

MEASUREABLE LEARNING OUTCOMES

By the end of this module students should be able to demonstrate an understanding of:

  • The theoretical and practical application of uni-, bi- and multi-variate statistical methods in data exploration and hypothesis testing;
  • The practice of data processing using various numerical software applications;
  • The analytical and writing skills required for the completion of a thorough statistical analysis of a relevant data set and the coherent presentation of the results in a written report.

EXAMPLES OF LECTURE/SEMINAR TITLES/TUTORIALS

Our lectures/seminars are highly participative and taught by leaders in their field.

  • Introduction: samples and variables, SPSS tutorial
  • Descriptive stats/ presenting data
  • Introduction to inferential statistics, testing and association (X2 test)
  • Testing for differences (t-test) and non-parametric tests
  • Analysis of Variance (ANOVA) and non-parametric tests
  • Disciminant analysis and Cluster analysis
  • Correlation and Regression
  • Principal components analysis vs Correspondence analysis

STUDENT ATTENDANCE AND INDEPENDENT STUDY

Type

Hours

Lectures 10
Laboratory Sessions 10
Independent Study (including preparation for assessments) 130

ASSESSMENT

Method

% of marks

Hours/Length

Project 100% 3000 words