Postgraduate Course: Quantitative Methods and Reasoning in Archaeology (PGHC11462)
|School||School of History, Classics and Archaeology
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This course introduces students to apply quantitative reasoning to archaeological case studies. It will provide a global perspective of current applications in the field covering descriptive statistics and hypothesis testing. Students will become proficient users of the open source platform R while developing critical skills on the use of statistics within archaeological projects.
The course will explore the key theoretical, methodological and technical aspects of quantitative archaeology. Through a mixture of lectures, practicals, in-class discussions, and projects the students will learn to identify and interpret patterns found in the archaeological record within a diversity of fields and specializations. They will also become aware of the potentials and limitations of statistics specifically linked to the study of the past, including topics such as time and uncertainty.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2019/20, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 11,
Seminar/Tutorial Hours 11,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||1. Research project (50%) - 2000 words
A research projects will be offered including a set of research questions, a dataset and some guidelines. The student will apply the methods learned to explore one of them.
2. Blog-based assessment (50%)
Students will write a blog entry at least once per block (i.e. 3 entries or more in total). The contents will be related to topics discussed in the lectures and practicals of the course. Students are also encouraged to comment on entries made by their peers.
Formative feedback will be given for each block entry including a mark for guidance. The final (summative) mark for this assessment will be based on the blog entries and comments made during the entire course.
||Students will receive verbal feedback during each practical and written feedback for the assessments following standard Learn procedure. They will also have the opportunity to discuss that feedback further with the Course Organiser during his published office hours or by appointment.
|No Exam Information
On completion of this course, the student will be able to:
- demonstrate the ability to test working hypotheses using statistics;
- demonstrate the ability to understand and critically analyse current applications of statistics in archaeology;
- demonstrate the ability to apply a wide range of methods to identify patterns in archaeological data;
- demonstrate critical understanding of the issues surrounding the investigation, interpretation and display of quantitative datasets and their links to social behavior;
- demonstrate independence of mind and initiative; intellectual integrity and maturity; an ability to evaluate the work of others, including peers.
|Drennan, R. D. (2010). Statistics for archaeologists: a commonsense approach (2nd ed). New York: Springer.|
Epstein, J. M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12.
Fletcher, M., & Lock, G. R. (2005). Digging numbers: elementary statistics for archaeologists (Vol. 33). Oxford Univ School of Archaeology.
Reinhart, A. (2015). Statistics Done Wrong: The Woefully Complete Guide. No Starch Press.
Shennan, S. (1997). Quantifying archaeology. University of Iowa Press.
Smith, M. E. (2015). How can archaeologists make better arguments? The SAA Archaeological Record, 18-23.
Verzani, J. (2014). Using R for introductory statistics. CRC Press.
|Graduate Attributes and Skills
||On successful completion of the course, students should be able to:
- gather, integrate and critically assess relevant information
- extract key elements and meanings from complex data sets
- answer a research question by developing a reasoned argument based on quantitative analysis
- present their ideas and analyses in a coherent fashion
|Course organiser||Dr Xavier Rubio-Campillo
Tel: (0131 6)50 3592
|Course secretary||Mr Jonathan Donnelly
Tel: (0131 6)50 3782