30060 - Bio-Demographic Lab

Learning outcomes

By the end of the course, students will be able to integrate methodological knowledge with the interpretation of bio-demographic phenomena, carry out the different stages of statistical analysis, and present the results of their analyses both in writing and orally.

Course contents

Research design: formulation of research questions, definition of the study design, and selection of the most appropriate methods and data sources.

Introduction to the principles of causal inference applied to the study of socio-demographic and bio-demographic phenomena.

Advanced use of R for data management, analysis, modelling, and visualisation.

Acquisition, integration, cleaning, and preparation of secondary data from different sources.

Application of statistical techniques to the analysis of socio-demographic and bio-demographic phenomena.

Visualisation of relationships between variables, spatial data, network structures, and the results of complex statistical models.

Critical assessment of sources, data quality, and the results of statistical analyses.

Effective communication of findings through reports, graphical representations, and oral presentations.

Readings/Bibliography

All resources will be made available on Virtuale.

Quantitative Social Science: An Introduction. Kosuke Imai, Princeton University Press

https://press.princeton.edu/books/hardcover/9780691167039/quantitative-social-science

Fundamentals of Data Visualization, Claus O. Wilke

https://clauswilke.com/dataviz/

Data Visualization: A Practical Introduction. Kieran Healy, Princeton University Press Link

https://socviz.co/

Data Visualisation: A Handbook for Data Driven Design. Andy Kirk, Sage

https://uk.sagepub.com/en-gb/eur/data-visualisation/book266150

Hands-On Programming with R. Garrett Grolemund

https://rstudio-education.github.io/hopr/

R Graphics Cookbook, 2nd edition. Winston Chang

https://r-graphics.org/

Teaching methods

Lectures, group work, in-class exercises, and original analyses of socio-demographic data using R.

Students who do not attend classes are requested to contact the instructor for specific guidance on the course materials.

Assessment methods

During the laboratory sessions, students will be required to prepare, either individually or in groups, a report that progressively applies the concepts, tools, and methods covered in the course. The results must be presented both in writing and orally. Detailed instructions on how to prepare and present the report will be provided on the Virtuale platform and explained during the laboratory activities.

The examination is not graded numerically but is assessed on a pass/fail basis.

Students who do not attend classes are asked to contact the instructor for clarification regarding the presentation requirements for the final report.

A limited, declared, and non-substantive use of AI is permitted in the preparation of the report for support activities such as summarising and rephrasing.

Students with specific learning disabilities, temporary disabilities, or permanent disabilities are advised to contact the relevant University office well in advance (https://site.unibo.it/studenti-con-disabilita-e-dsa/en ). The office will propose any appropriate accommodations, which must be submitted to the instructor for approval at least 15 days in advance. The instructor will assess their suitability in relation to the learning objectives of the course.

Teaching tools

Laboratory activities will be conducted using RStudio. All scripts, datasets, teaching materials, and supplementary readings will be made available on the Virtuale platform.

The practical sessions will also include peer-to-peer learning activities designed to encourage discussion among students, the sharing of analytical strategies, and collaborative problem-solving.

Office hours

See the website of Saverio Minardi