95704 - ANALYSING CULTURAL CHANGE AND CULTURAL TRANSMISSION: A METHODOLOGICAL PERSPECTIVE

Learning outcomes

Human culture is the result of cumulative processes of transmission, innovation, environmental and socio-economic pressure, human migrations, and the exchange of objects and ideas at different spatial and temporal scales. The course focuses on both theoretical and methodological questions raised by current approaches to the study of cultural change, and adds a focus on the basic statistical tools currently used to analyze data generated in the fields of cultural heritage, archaeology, anthropology, and other social sciences. By the end of the course, the student has acquired skills that facilitate the systematic description of empirical evidence and the exploration of diversity in different contexts. At the same time, the student understands the basics of hypothesis testing in the light of broader cultural and anthropological processes, and becomes aware of good and bad practices related to the use of quantitative methods in the social sciences.

Course contents

The course entails frontal lectures covering theoretical and methodological issues followed by an overview of specific techniques during guided (both individual and group) practical sessions. More 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, sociology and economics; their abandonment due the diffusion of post-modern epistemology; the reinstatement of quantitative knowledge in contemporary research.

2. Uncertainty, equifinality, and underdetermination. All disciplines that aim to understand and explain invisible/fragmentary processes based on visible/accessible patterns have to come to terms with the abundance of plausible scenarios and problems of resolution and scale. How can we effectively deal with them?

3. 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 Cultural Evolutionist paradigm in Anthropology and Archaeology, its impact on today’s European perspective, and the later shift towards a different paradigm informed by population genetics. Comparison with postmodern approaches, qualitative approaches, and active contrast between written sources in group discussion sessions.

4. 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 archaeologist Flinders Petrie have in common? Introduction to the concepts of unbiased cultural transmission, frequency seriation, the concept of fashion cycles, and their relationship with models developed outside the social sciences.

5. Divide et impera: the importance of controlling your taxonomy. Researchers interested in human culture often have to categorise continuous variables in order to compare them with groups of interest. This endeavour often leads to frustration and forces us to leave the particular for the general. 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.

6. Lost in translation. Terms and symbols used in mathematics and informatics are simply another, more formal type of expression 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 fundamental tenets of probability theory. The concepts of power, base rate, effect size, and p-hacking.

8. Measuring shadows in Plato’s Cave to understand the world outside. Information, uncertainty and models of knowledge. Variables and constants. Scales of measurement. Accuracy. The types of variables which social scientists, archaeologists, anthropologists, and art historians usually have to treat. Descriptive statistics, basic graphical representations (bar charts, histograms, boxplots).

9. We value diversity. Anthropologists, archaeologists, and social scientists are interested in studying change in human culture 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, over time, and across space.

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. 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.

12. 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.

13. 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.

14. All models are wrong. To what extent is your one useful? What does it mean to create a model? How can we use it to understand something more about reality? Even just a single line can be 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

To be defined

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.

Teaching methods

Frontal lectures

Individual practical sessions and presentations / group practical sessions and presentations

Discussion on selected papers

Language: English

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 two figures and one tables) structured as a research article for scientific journals, presenting the results of a review or analyses carried out on a given dataset to answer given 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 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).

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