87402 - Quantitative Methods for the Interpretation of Cultural Heritage

Learning outcomes

The course focuses on both theoretical and practical aspects of the basic statistical analyses currently used to quantitatively analyze data generated in the field of cultural heritage, archaeology, anthropology, and demo-ethnography. During the course, the student acquires skills that facilitate the systematic description of empirical evidence and the quantification/exploration of variability observed in the data. At the same time, the course introduces the student to formal hypothesistesting and to interpreting results in the context of broader cultural and anthropological processes.

Course contents

No previous knowledge of methods and softwares used in the course is required. A good level of English and access to a laptop with wi-fi connection are desirable.

The course entails frontal lectures covering theoretical and methodological issues followed by an overview of specific techniques during guided practical sessions. In detail, the course will be articulated as follows:

1. Quantitative methods: why bother? Introduction to contents and aim of the course. Overview of the emergence and diffusion of quantitative methods, their first appearance in archaeology and anthropology, biology, and economics; their abandonment due the diffusion of post-modern epistemology; the reinstatement of quantitative knowledge in contemporary research.
2. The Transmission of Culture, from Evolutionism to Evolution. Thoughs, ideas, and objects clearly undergo a process of change over time and space. Quantitative methods help with identifying traces of such change. Overview of the emergence and development of the Cutural Evolutionist paradigm in Anthropology and Archaeology, its later shift towards a different paradigm informed by population genetics.
3. Morelli’s ear and Petrie’s vessels. What do the method established by the famous art connoisseur Giovanni Morelli and the seriation of ancient Egyptian ceramics elaborated by Flinders Petrie have in common? Introduction to the concepts of unbiased or non-selective cultural transmission, frequency seriation, the concept of fashion cycles, and their relationship with non-archaeological models.
4. Type or typology? Researchers interested in material culture or anthropological remains often have to count great quantities of objects or even greater quantities of trait expressions forming objects or individuals. To avoid sinking in the sea of variability generated by such a practice, they have to ordinate their data in ways that are consistent with their own research questions and yet are repeatable and can be tested by others. Overview of the many techniques developed over time for classifying and grouping elements in spite of the noisy variability characterising a dataset.
5. Time unveils the Truth. Time is a key element in archaeological and anthropological research. How is it measured? Overview of strengths, drawbacks, measurement errors and uncertainty linked to models used to date different materials found in archaeological and anthropological contexts. How to correctly read, interpret, and report dates we find in published pieces of research. Relationship between dates, stratigraphy, and demography.
6. Lost in translation. Terms and symbols used in mathematics and informatics are simply other, more formal expressions we can learn in order to exchange information with other scientific disciplines. Trying to learn at least the basic grammar can free us from depending on translators, and can help us understanding the potential concealed in our dataset. Overview of available statistical software and languages. Introduction to R, software installation. Introductory practical session.
7. It’s possible. Even better, it’s probable. Introduction to the concept of probability and the importance of expressing concepts in probabilistic terms. Definitions of probability developed by different statistical approaches. Communicating probability, difficulties and possible solutions. The three fundamental tenets of probability theory.
8. Measuring shadows in the Cave to understand the outside world. Information, uncertainty and models of knowledge. Variables and constants. Scales of measurement. Accuracy. The types of variables which archaeologists, anthropologists, and art historians usually have to treat. Descriptive statistics, basic graphical representations (bar charts, histograms, boxplots).
9. Valuing diversity. Anthropologists and archaeologists are interested in studying change over time and space. In order to appreciate such change they need to use appropriate measures of diversity that can be easily compared against each other.
10. What do you mean by normal? The normal distribution, its history, scientific relevance, and implications. Measures of central tendency and dispersion. The importance of visually inspecting a dataset. Some examples of non-normal distributions. Visualization and use of the normal distribution in R.
11. When a part refers to the whole. We are often bound to study fragments, remnant distributions, and traces of past activities. Available time and resources are always scarce. We have to quickly decide what is relevant and which problems we will have to face. Statistics can help us to design sampling strategies with which we can more efficiently plan field activities and research priorities.
12. Does the exception prove the rule? We are interested in understanding why some observations do not conform to our expectations. How can we establish what is different or extreme enough to catch our attention? Introduction to hypothesis testing and exploration of the three main approaches to probability. Methods for comparing groups of observations.
13. Correlation ≠ causation. Determining the relationship between variables does not automatically entail causality. In any case, understanding and measuring association between variables is always the first step.
14. The map is not the territory. We can use a compass if we do not want to get lost. There are methods developed to ordinate and synthesize data containing lots of variables, to avoid getting lost in the clouds of their variability. Conceiving and exploring a multi-dimensional space of variation.
15. A line is a model. Shifting from measuring the relationship between variables to measuring the effect that one variable has on another (i.e. establishing a causal relationship) makes us take a big step forward in our understanding of any process of change. Assumptions of linear regression. Importance and limits of models.

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

For students who do not attend classes

Carlson, D.L. 2017. Quantitative Methods in Archaeology Using R, Cambridge, Cambridge University Press

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

 

Suggested readings

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

There will be a two-step evaluation process:

1. Submission of a short essay (1500 words max and at least 2 figures) presenting the results of analyses carried out on chosen datasets to answer two research questions. 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 evaluation)
2. Oral examination 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 evaluation)

Teaching tools

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

Office hours

See the website of Eugenio Bortolini