Quantitative methods
Quantitative methods bring a highly systematic and rigorous approach to data analysis and are a powerful tool for social scientists to use when testing their theories or undertaking initial exploratory analysis. This theme is open to all doctoral students that are interested in using established and cutting edge quantitative methodologies and data in their research.
The stream will evaluate the benefits and limitations of different types of quantitative methods and provide a support network for members to discuss and find solutions to the challenges they are facing.
Themes to expand upon include (but are not limited to):
- operationalising concepts in a quantitative analysis;
- ensuring that research projects rooted in quantitative methodologies remain theory and topic driven;
- how to choose between competing analytical techniques and determine the most appropriate approach for your study;
- balancing theoretical and pragmatic decisions in your research design;
- social network analysis and network models;
- agent-based models and computer simulations;
- computational social science;
- dealing with imperfect data and the compromises this may entail to the overall research design;
- techniques for dealing with missing data;
- dealing with large and unstructured flows of new data available via digital technologies and platforms, i.e. Twitter and Facebook; and
- understanding the design principles behind citizen social science methodologies.
Academic Lead for 2021/22: Dr Tomas Diviak : tomas.diviak@manchester.ac.uk
Dr Tomáš Diviák currently holds a position of Presidential Fellow at the department of criminology and the Mitchell Centre for Social Network Analysis at the University of Manchester. I also collaborate with the department of sociology, Faculty of Arts at Charles University and with the Centre for Modelling of Biological and Social Processes. Besides that, I am a co-founder of Czech Network for Social Network Analysis, through which we regularly organize workshops and conferences. My research focuses mainly on social network analysis (SNA), most prominently statistical models for network data, and on analytical sociology and criminology. I am interested in the application of SNA, mainly to criminal networks, but also to political, organizational, health-related, or historical networks. I find the study of structure and dynamics of human networks to be intriguing and crucial for our understanding of social reality. Besides network analysis and sociology (and science in general), I enjoy reading science fiction & fantasy books, lifting heavy weights, listening to heavy music, taking long walks, and playing various card games.
