During the first year of the doctoral programme, students engage in a set of core courses that lay a strong foundation in the discipline, covering critical thinking regarding sociological theory and practices and a wide spectrum of quantitative, qualitative, and computational research methods and techniques.
In parallel, the first year incorporates practical training through research tools, including academic writing techniques and specialized software packages.
Core courses
Thinking Sociologically (48 hours):
This course cultivates critical thinking regarding sociological theory and practices. It offers an in-depth exploration of epistemological options, macro, micro, and meso perspectives, drawing from seminal sociological research. Through active participation and argumentation, students develop a nuanced understanding of sociological debates.
Research Design (24 hours):
Focused on the fundamental principles of research design, this course spans quantitative, qualitative, and mixed-methods traditions. Emphasizing the transition from research objectives to methodological choices, it delves into epistemological issues, data selection (primary vs. secondary), the role of experiments in social research, and various logical approaches to case studies.
Advanced Methods for Social Research (84 hours):
This course provides advanced training in data collection and analysis for social research, with emphasis on interpreting data through a theoretical lenses. It comprises four sub-modules:
- Computational Modelling for Social Research: Covers digital data analysis using machine learning, natural language processing, and simulations.
- Advanced Quantitative Methods: Focuses on longitudinal and causal analysis of quantitative data.
- Advanced Qualitative Methods: Addresses the collection and analysis of qualitative data, emphasizing their integrated analysis and theorization.
- Methods for Social Network Analysis: Prepares students for collecting and analyzing relational data.
Tools
Research Development Lab:
The lab, spanning the first year, introduces doctoral students to the department, fosters networking among faculty and students, and identifies individual training needs through a 'training needs assessment'.
Advanced Software Packages for Sociological Data Analysis:
This module introduces data analysis software packages widely used in social research e.g., Atlas.ti, Elan, Nvivo, Python, R, Stata, and Ucinet.
Academic Writing for the Social Sciences:
A 36-hour course, conducted by the University's language center, guides candidates in crafting scientific articles tailored for social sciences, working on their thesis chapters or article drafts.
Research Proposal for National and International Programs:
Involves participation in various events, seminars, and webinars related to national and international research funding programs (e.g., Horizon, MSCA, Fulbright).
Science Communication Lab:
Aiming to broaden candidates' awareness about disseminating research results in academia and beyond, this lab provides insights into public engagement evaluation and skills for effective public involvement assessments.
Research Ethics for Social Sciences Lab:
This lab addresses ethical considerations in social research, covering replicability, verification, transparency, and privacy concerns. Candidates are encouraged to attend seminars on research integrity, focusing on principles, risks, project writing, and ethical committee approvals with interdisciplinary perspectives.