B1729 - Statistical Analysis in Archaeology (1) (LM)

Learning outcomes

At the end of the course the student knows the use of quantitative methods increasingly relevant in Archaeology and in the archaeological literature. Understanding statistical tests and techniques is essential for correctly analysing collected data, testing hypotheses, and making comparisons across different sets of archaeological evidence. This course aims to provide an introduction to basic statistical principles via their application in archaeological contexts through practical examples. The course will also include an overview of the use of free and open-source statistical software.

Course contents

No prior knowledge of the methods or software used in the course is required. Students are, however, expected to have a sufficient command of the English language and access to a computer with an internet connection.

The course consists of a series of lectures addressing theoretical and methodological issues, in some cases followed by the explanation of specific methods and their practical application by students. Practical activities will be carried out under the continuous guidance of the instructor and will be based on case studies specifically designed and presented for the course. The course is structured as follows:

1. Introduction to the content, methods, and objectives of the course. Overview of the origins and dissemination of quantitative research methods, their early applications in anthropology, archaeology, biology, and economics, their decline with the spread of post-modern approaches, and the subsequent re-emergence of quantitative data in research.

2. Cultural transmission between evolutionism and evolution. Thoughts, ideas, and their material expressions clearly demonstrate that culture is subject to change over time and space. Quantitative methods assist in identifying traces of such change. Overview of the emergence and development of cultural evolutionism in anthropology and archaeology, its critique, and the rise of population-based approaches.

3. Introduction to the concept of non-selective (neutral) transmission, frequency seriation as developed by archaeologists, the concept of fashion, and its relationship to models developed outside archaeology and anthropology.

4. Overview of the various approaches developed over time to classify and group elements emerging from archaeological, historical, and anthropological contexts. The concepts of type and typology.

5. Overview of statistical software currently available. Theoretical and practical introduction to R, including software installation. Practical exercise focused on installation and initial use.

6. Introduction to the concept of probability and the importance of expressing concepts in probabilistic terms. Complementary definitions of probability developed within different theoretical approaches. Communicating probability: challenges and possible solutions. Different representations of probability. Fundamental rules of probability theory.

7. Information, uncertainty, and models of knowledge. Variables and constants. Scales of measurement. Accuracy. Types of variables commonly encountered by archaeologists, anthropologists, and art historians. Descriptive statistics and basic graphical representations (bar charts, histograms, box plots).

8. Measures of diversity and distance as tools for quantifying variability among individuals and groups.

9. Introduction to the normal distribution, its historical development and scientific significance, and its implications. Measures of central tendency and dispersion. The importance of data visualisation. Non-normal distributions and common examples. Visualisation and use of the normal and other distributions in R.

10. Much research focuses on fragments and remnants of past activities. Time and resources are always limited, requiring rapid decisions about what merits further investigation and what may be more problematic. Statistics provides sampling strategies that enable the planning of field activities.

11. Introduction to hypothesis testing and exploration of the three most widely used approaches. Methods for comparing individuals and groups.

12. Measures of association and correlation (correlation ≠ causation).

13. Multivariate analyses and methods for ordering and simplifying data with multiple variables.

14. Definition of models, their applications, and their inherent limitations. Introduction to linear regression.

15. Introduction to non-linear models and methods for exploring relationships between matrices.

Readings/Bibliography

The program of the course is the same for both students attending and not attending. Owing to the nature of the course, frequency of the lessons is strongly recommended. However, students who for valid reasons cannot attend the course are invited to contact the
teacher, during the office hours, for the suggestion of potential supplementary texts.

Additional teaching material and lecture presentations will be provided during the course.



Compulsory readings (parts indicated during the course)

Renfrew, C. e Bahn, P. 2017. Archeologia, Teoria, metodi, pratica, Zanichelli

Clarke, D. L., 1998. Archeologia Analitica, Mondadori Electa

# Introduction to R
Mineo, A.M. 2003. Una Guida all’Utilizzo dell’Ambiente Statistico R, disponibile al link https://cran.r-project.org/doc/contrib/Mineo-dispensaR.pdf

# Seriation
Dunnell, R. C., 1970. Seriation Method and its Evaluation. American Antiquity, 35, 305–319. DOI: 10.2307/278341

# Paradigmatic classification (classificazione monotetica)
O’Brien, M. J., Lyman, R. L., Saab, Y., Saab, E., Darwent, J. and Glover, D. S., 2002. Two Issues in Archaeological Phylogenetics: Taxon Construction and Outgroup Selection. Journal of Theoretical Biology, 215, 133–150 https://doi.org/10.1006/jtbi.2002.2548

# Using C14 sums to infer demographic trends
Palmisano, A., Bevan, A., Shennan, S. 2017. Comparing archaeological proxies for long-term population patterns: An example from central Italy, Journal of Archaeological Science 87:59-72


# Da Renfrew, C. e Bahn, P. 2018. Archeologia: Teoria, Metodi, Pratica (Terza edizione italiana), Archeologia Zanichelli
1) Sui Primi tentativi di sistematizzazione nello studio del passato pp. 8-27
2) Su Datazione relativa: pp. 121-125
3) Su Datazione assoluta - solo Dendrocronologia e 14C - pp. 132-144
4) su Inferenza in archeologia: approcci diversi tra processuale, post-processuale, marxista, evolutivo, approccio popolazionistico e focus su individuo: pp. 485-499

or equivalent chapters in a recent English edition

# Classification and grouping
Bortolini, E. Typology and Classification, in: The Oxford Handbook of Archaeological Ceramic Analysis, Oxford, OXFORD UNIVERSITY PRESS, 2016, pp. 651 - 670


D.L. Clarke 1978 (2nd edition) Analytical Archaeology, Methuen and Co LTD:London
Capitoli
- Introduction (pp.30-41)
- Cap. 4 Material Culture Systems - Attributes and Artefacts
- Cap. 5 Artefact and Type
Equivalenti nell'edizione in italiano Archeologia Analitica - 1998, Mondadori Electa:
- Introduzione (pp.35-42)
- Cap 4 Sistemi di Cultura Materiale - Attributo e manufatto
- Cap 5 Manufatto e tipo

# Levels of measurement
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 2 Quantifying Description

# Basic graphs
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 3 Picture Summaries of a Single Variable

# Descriptive statistics
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 4 Numerical Summaries of a Single Variable

# The normal distribution
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 8 Numeric Variables: the Normal Distribution

# Chi-squared test
Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press
- Cap 6 The Chi-squared test

# The same contents can be explored in Italian in the volume:
Agresti, A., Franklin, CA. 2016. Statistica: L'arte e la scienza d'imparare dai dati, Pearson.



For students who do not attend classes
Carlson, D.L. 2017. Quantitative Methods in Archaeology Using R, Cambridge, Cambridge University Press - Part I, chapters 1-9

Shennan, S. J., 1997. Quantifying Archaeology (2nd edition). Edimburgh: Edimburgh University Press.



Suggested readings

Agresti, A., Franklin, CA. 2016. Statistica: L'arte e la scienza d'imparare dai dati, Pearson.

Borra S., Di Ciaccio A. 2021. Statistica. Metodologie per le scienze economiche e sociali, McGraw-Hill.


Mineo, A.M. 2003. Una Guida all’Utilizzo dell’Ambiente Statistico R, disponibile al link https://cran.r-project.org/doc/contrib/Mineo-dispensaR.pdf

Dunnell, R. C., 1970. Seriation Method and its Evaluation. American Antiquity, 35, 305–319.

Dunnell, R. C., 1971. Systematics in Prehistory. Caldwell, New Jersey: The Blackburn Press.

Fletcher, M. and Lock, G. 2005. Digging numbers (2nd edition), Oxford, Oxford University School of Archaeology

Legendre, P. and Legendre, L., 1998. Numerical Ecology (Second English Edition). Elsevier.

Leonard, R. D. and Jones, G. T. (eds.), 1989. Quantifying Diversity in Archaeology. Cambridge University Press: Cambridge.

Lyman, R. L. and OBrien, M. J., 2006. Measuring Time with Artifacts: A History of Methods in American Archaeology. Lincoln: University of Nebraska Press.

Madrigal, L. 2012 (2nd edition) Statistics for Anthropology, Cambridge, Cambridge University Press

Mesoudi, A. 2011. Cultural Evolution: how Darwinian theory can explain human culture and synthesise the social sciences, Chicago, University of Chicago Press

Reinhard, A. 2015. Statistics done wrong. The woefully complete guide, No Starch Press

Teaching methods

Frontal lecture
Individual practical sessions/ group practical sessions
Language: Italian. English, however, can be integrated or used interchangeably upon request by international students.

Assessment methods

Students who are not able to attend classes will be evaluated in the same way as those who will attend. In case of doubts or questions students can directly contact the course coordinator for clarifications. Details on datasets, essay structure, and research questions for examination will be offered during classes and contained in the teaching materials available on the online web page of the course.

Evaluation for this course will consist of two steps:

1. Submission of a short essay (2000 words max and at least four figures and two tables) structured as a research article for scientific journals, presenting the results of analyses carried out on a given dataset to answer nine research questions. Missing or wrong answers will be penalised with a subtraction of max. 2 points per research question from a maximum total score of 30 points. The text can be written in Italian or English. The essay will provide evidence of successful acquisition of concepts and skills linked to the effective presentation of data, methods, and results (70% of final grade). Submission of essays by the appropriate deadline (detailed on AlmaEsami, usually ~7 days before the date of the oral examination) is mandatory to enroll for the following oral examination.

2. Oral examination (monthly exam dates are published each semester) in which the student is required to discuss the essay and to answer questions emerged on its content. Other themes and concepts explained during the course can also be discussed (30% of final grade. This second examination will confirm or change
(either positively or negatively) the evaluation of the essay.

The final grade will be based on the level of methodological knowledge acquired during the course, on the accuracy with which instructions on formatting and essay structure have been followed, on the degree of independence expressed by the interpretation of results in light of available data and research questions.

A high degree of detail and precision in the acquired methodological skills, innovative/independent/critical thinking, the search for independent solutions, and creativity in interpreting results will be positively evaluated with the highest marks (30-30L). Superficial factual knowledge will instead be considered as almost irrelevant.
Proven knowledge of the themes and techniques presented during the course, good analytical skills and critical thinking, good/appropriate use of terminology and language - despite the presence of errors in the written essay - will lead to good and very good final evaluations (27-29). Basic competence necessary to answer at last some of the proposed questions, basic analytical skills and good mastery of technical terms and language will be positively evaluated (23-26). Finally, a sufficient level in terms of analysis and language, and the presence of non-trivial errors in the essay will guarantee a passing grade (18-22).

Be informed that the undeclared use of generative artificial intelligence is considered a form of plagiarism (and not a particularly clever one).

Teaching tools

Additional teaching material and lecture presentations will be available on the online course web page.

Students with a specific learning profile or a temporary or permanent disability, may get in touch with the Student Disability and DSA Office as soon as possible: https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students . They will help identify any accommodations they may need.

To ensure we can implement any adjustments in time, please submit any requests at least 15 days before the exam date to the course coordinator. The coordinator will review the expressed needs in light of the course objectives and confirm the appropriate arrangements.


Students are also warmly encouraged to contact the course coordinator early on, ideally at the start of the term, to co-design effective strategies for engaging with class activities and course materials.

Office hours

See the website of Eugenio Bortolini