Module Code - Title:
PS6142
-
NETWORK ANALYSIS
Year Last Offered:
N/A
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
Rationale and Purpose of the Module:
This module provides a comprehensive introduction to network theory and methods, combining theoretical foundations with practical training in R. Students will learn how to conceptualise social phenomena in terms of networks, apply appropriate analytical methods, and critically interpret results. The module emphasises applications across psychology, sociology, and related social sciences.
Syllabus:
The module covers key aspects of network analysis, including:
- Introduction to network thinking, data structures, and visualisation
- Centrality measures, blockmodelling, and role detection
- Community detection and meso-level structures
- Statistical approaches, including QAPs and ERGMs
- Longitudinal analysis using Stochastic Actor-Oriented Models (SAOMs)
- Applications of network analysis to correlation and co-occurrence networks
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
- Explain core concepts and methods of network analysis.
- Apply network analysis methods to social science research questions.
- Demonstrate how to store, manage, and visualise network data in R.
- Evaluate network analytic results, interpretations, and their theoretical implications.
Affective (Attitudes and Values)
On successful completion of this module, students will be able to:
- Appreciate the value of network approaches in social sciences.
- Engage confidently with network data and research applications.
Psychomotor (Physical Skills)
On successful completion of this module, students will be able to:
- Conduct statistical analyses of cross-sectional and longitudinal network data using R.
- Use specialised R packages (e.g., igraph, sna, statnet, RSiena, qgraph) to analyse and visualise network data.
How the Module will be Taught and what will be the Learning Experiences of the Students:
The module consists of weekly lectures, labs with hands-on training in R, and literature seminars based on student-led presentations and critical discussions of recent examples of applied research. Students will complete an individual project applying network methods to empirical data. While the modules does promote the development of all UL graduate attributes, curious, knowledgeable, and flexible stand out as particularly relevant to engage with network methods, how they fit with substantive research questions, and their multiple potential applications for social science research.
Prime Texts:
Borgatti, S. P., Everett, M. G., Johnson, J. C., & Agneessens, F. (2022)
Analyzing Social Networks using R
, Sage
Rawlings, C. M., Smith, J. A., Moody, J., & McFarland, D. A. (2023)
Network Analysis: Integrating Social Network Theory, Method, and Application with R
, Cambridge University Press
Van der Maas, H. L. J. (2024)
Complex-systems research in psychology
, SFI Press
Other Relevant Texts:
Kolaczyk, E. D., & Csárdi, G. (2014)
Statistical Analysis of Network Data with R
, Springer
Robins, G. L. (2015)
Doing social network research: Network-based research design for social scientists
, SAGE
Programme(s) in which this Module is Offered:
MASTER OF SCIENCE IN APPLIED SOCIAL DATA ANALYTICS
Semester(s) Module is Offered:
Spring
Module Leader:
Srebrenka.Letina@ul.ie