Digging Data: quantitative data analysis in Archaeology

Module overview

Archaeology is an immensely data-rich activity that records the characteristics of sites, landscapes and artefacts, sometimes in great detail. Making sense of that data often relies on quantitative or statistical methods to identify patterns, associations and relationships. This module aims to provide students (who do not necessarily have a recent background in maths or statistics) with some statistical concepts and methods, and the knowledge to apply them using readily available software (spreadsheets). It aims to deliver understanding of a range of ideas about quantitative approaches to archaeology from how to make better graphs to how we can phrase archaeological questions in a range of quantitative ways. During the module, you will learn about graphical representations of numerical data, descriptive statistics and summaries of single variables; the normal distribution; statistical inference; some measures of association between two variables and ways to explore relationships between numeric variables using correlation and regression.

Aims and Objectives

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Express archaeological questions in quantitative ways
Understand some key statistical concepts
Evaluate, describe and analyse archaeological datasets

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Describe archaeological quantitative data using graphs
Perform correlation and regression analyses
Work effectively with functions and expressions in spreadsheets
Use a computer to undertake numerical analysis
Perform simple standard statistical tests for association and significance

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Numerical and statistical description of single variables
The normal distribution and sampling
Correlation and regression
Statistical inference and significance

Learning and Teaching

Teaching and learning methods

Numerical and statistical concepts and methods are introduced in lectures, which are supported by computer- based practical classes to reinforce learning. Short statistical exercises are set to be undertaken outside of contact hours. The module also expects students to develop skills in spreadsheets (formulae, functions, numerical data processing). Because of the different level of students’ skills on entry to the module, students are guided to self-led online resources for this purpose. Teaching methods include - Online and offline courses and resources (for spreadsheet skills) - Lectures - Computer-based practical classes - Project surgeries Learning activities include - Set reading and statistical exercises - Independent work in preparation for data analysis project - Exam preparation

Study time
Type	Hours
Completion of assessment task	20
Preparation for scheduled sessions	30
Practical classes and workshops	24
Project supervision	4
Revision	20
Follow-up work	30
Wider reading or practice	10
Lecture	12
Total study time	150

Resources & Reading list

Textbooks

Thomas, DH (1986). Refiguring Anthropology. Illinois: Waveland Press.

Baxter MJ (2003). Statistics in archaeology. London: Arnold.

Fletcher, M and Lock GR (1991). Digging numbers. Oxford: Oxford University Committee for Archaeology.

Orton, C (2000). Sampling in Archaeology. Cambridge: Cambridge University Press.

Tufte ER (1983). The visual display of quantitative information. Cheshire, Connecticut: Graphics Press.

Shennan, SJ (1997). Quantifying Archaeology. Edinburgh: Edinburgh University Press.

Drennan RD (1996). Statistics for archaeologists: a commonsense approach. New York: Plenum.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Exercise

Assessment Type: Formative
Feedback:
Final Assessment: No
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Test	30%
Data analysis project	70%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Data analysis project	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Mid-term test	30%
Data analysis project	70%

Repeat Information

Repeat type: Internal & External