Checking date: 30/04/2024


Course: 2024/2025

Research Design for Social Sciences
(19134)
Master in Computational Social Science (Plan: 472 - Estudio: 375)
EPC


Coordinating teacher: VILLAMIL FERNANDEZ, FRANCISCO

Department assigned to the subject: Social Sciences Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Objectives
- Knowledge of the principles of scientific research. - Ability to discern among refutation, confirmation and contrastation. - Knowledge of different research designs: variable-based, case-based, comparative. - Ability to combine different research designs. - Be familiar with the different types of scientific evidence. - Ability to explain the principles underlying statistical models for social networks.
Skills and learning outcomes
Description of contents: programme
1. Introduction - Importance of research design in social sciences - What it means to answer a question with empirical evidence - Types of empirical research 2. Research questions - How to find them and how to work with them - Description and explanation - Variables and relationships between variables 3. Types of research design and empirical information - Case studies, comparative studies, quantitative studies with large samples - Advantages, disadvantages and complementarities - Types of empirical evidence - Unit of analysis and variability - Measurement problems 4. Causality - Prediction, correlation, probability - Causal effects and causal mechanisms - Mechanism-based explanations - Levels of explanation: macro, meso, micro - Directed Acyclic Graphs 5. Problems in causal inference - Confounding, selection bias, collider bias, etc. - Problems of internal and external inference - Ecological fallacy, diffusion, external validity, etc. 6. Research design to identify causal relationships - Ideal of experimental method in natural sciences - Counterfactuals and how to approach them - Advantages and disadvantages of causal inference design 7. Introduction to the logic of causal inference methodology - Understanding the most common techniques: experiments, difference-in-differences, RDD, matching, etc.
Learning activities and methodology
Training Activities: - Theoretical-practical classes - Laboratory practical sessions - Tutorials - Group work - Individual student work - Partial and final examinations Teaching Methods: - Presentations in the professor's lecture room with computer and audiovisual support, in which the main concepts of the subject are developed and a bibliography is provided to complement the students' learning. - Critical reading of texts recommended by the subject professor: Press articles, reports, manuals and/or academic articles, either for later discussion in class, or to expand and consolidate knowledge of the subject. - Resolution of practical cases, problems, etc. raised by the professor, either individually or in a group. - Presentation and discussion in class, under the moderation of the professor, of topics related to the content of the subject, as well as practical case studies. - Developing pieces of work and reports, individually or in group.
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60

Basic Bibliography
  • Ethan Bueno de Mesquita & Anthony Fowler. Thinking clearly with data: A guide to quantitative reasoning and analysis. Princeton University Press. 2021
  • Nick Huntington-Klein. The Effect: An Introduction to Research Design and Causality. Chapman and Hall/CRC Publishing. 2021
  • Scott Cunningham. Causal Inference: The Mixtape. Yale University Press. 2021

The course syllabus may change due academic events or other reasons.