Network Analysis

SOST30022 Network Analysis 20 Semester 2 Undergraduate Level 3 Yan Wang Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 The basic premise of this course is that the social world is relational. We cannot ignore that we are influenced by people we know, have met and respect; ideas and allegiances are formed and maintained in social settings and organisations; not all people have equal opportunities when it comes to finding a job; we communicate over networks, be they online or offline; etc. In this course we aim to produce a detailed understanding of the web of social contacts  that structure our daily life and society. We consider network structure both interesting in its own right and something that creates co-dependencies between social units in terms of outcomes and properties of these social units themselves. The overarching goal of the course is to provide students with statistical tools that bridge concepts on the one hand, and what we can observe in the data – on the other hand. Put another way, we aim to avail ourselves of approaches that permits us to test if theoretical ideas about social relations are supported by empirical observations of relations between people, organisations, countries, or other social units. The unit aims to:  Introduce a toolbox for empirical statistical investigation of theories on relations between social units.  Introduce the practical issues involved in managing and analysing network data.  Provide a concept- and research-driven perspective on everyday observables and the skills and knowledge to solve analytical puzzles in a wide array of applied contexts.  Give students a working handle on the basic network analysis tools.  Foster familiarity with analytical tools and methods at a level that enables students to further their skills in relevant areas.  Offer a statistical analytical framework for critical appraisal of quantitative statements in networks and related areas. Understand the empirical requirements and evidence needed for drawing conclusions about complex social processes involving network structures. Operate with fundamental concepts in network analysis, both theoretical and technical. Relate concepts such as micro-macro, self-organisation, structuring mechanism, and emergence to specific predictions and hypotheses for observables on network data. Choose the appropriate network-analytical approach for a particular set of relevant research questions.   Manage social network datasets and analyse network data with dedicated network-analytical software.    Visualise, describe, and report the results of social network analysis, drawing conclusions about related social processes.    Apply essential network-analytical concepts. Handle network data, interpret analytical results, and report them. The course involves lectures and computer workshops. The lecture component provides theoretical and methodological frameworks for learning about the analysis of social network data and the key pathways from theory to subjecting research questions to empirical scrutiny. The workshops are linked to the lectures and serve to give a concrete and hands-on perspective on the material taught. Furthermore, the workshops give students training in specific methodologies and embed practical skills. The workshops have an immediate goal of equipping students with the necessary skills and knowledge to complete the assignment. Canvas resources are used to enable students to access teaching data and data sources. Students are also provided with video materials of lectures and software tutorials.  0 Other 100% Written assignment (essay) 100%The word count must not exceed 2000 words. The essay must include a (1) network visualization and tables with (2) descriptive statistics, (3) statistical model and goodness of fit test, (4) interpretations of 1-3. All Social Statistics courses include both formative feedback - which lets you know how you're getting on and what you could do to improve - and summative feedback - which gives you a mark for your assessed work.  Basic knowledge of statistical analysis. Y Borgatti S., Everett M, Johnson J. (2018). Analysing Social Networks 2nd Ed, Sage, London Hanneman R.A. and Riddle M. (2005). Introduction to Social Network Analysis. Available at <a href="https://faculty.ucr.edu/~hanneman/nettext/" rel="noreferrer noopener" target="_blank">https://faculty.ucr.edu/~hanneman/nettext/</a> Lusher,D., Koskinen, J., and Robins, G. (2013). Exponential random graph models for social networks: Theory, methods and applications. Cambridge University Press Robins, G. (2015). Doing Social Networks Research: Network Research Design for Social Scientists. Sage.  Scott, J. (2000) Social Network Analysis: A Handbook, London, Sage Wasserman, S. and Faust, K. (1994) Social Network Analysis, Cambridge University Press Online Resources:Mitchell Centre <a href="http://www.ccsr.ac.uk/mitchell" rel="noreferrer noopener" target="_blank">www.ccsr.ac.uk/mitchell</a>  Methods@Manchester <a href="https://www.methods.manchester.ac.uk/" rel="noreferrer noopener" target="_blank">www.methods.manchester.ac.uk/</a>   0 0 170