Module SSIM906 for 2018/9
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
SSIM906: Quantitative Dissertation
This module descriptor refers to the 2018/9 academic year.
Module Aims
To enable you to write an extended piece of independent writing, around a topic of your own choosing using some of the quantitative data-analytic tools you became acquainted with during the programme (e.g., methods for causal inference, Bayesian econometrics, network analysis, text-mining and analysis techniques), in communication with key experts in your chosen area. It will allow you to demonstrate depth and breadth of knowledge in a particular subject area of professional or intellectual interest. The dissertation will be a mark of your ability to express yourself in writing.
On successfully completing the programme you will be able to: | |
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Module-Specific Skills | 1. Demonstrate knowledge in depth of a specialised subject area (may include a specific statistical technique) 2. Design an individual research programme, incorporating appropriate quantitative social science research methods 3. Collate and analyse primary or secondary data related to a subject discipline from appropriate sources. |
Discipline-Specific Skills | 4. Assimilate and critically analyse data from an appropriate range of sources, from primary or secondary data sets 5. Develop cogent argument and apply appropriate statistical techniques 6. Communicate complex information and ideas effectively in writing. |
Personal and Key Skills | 7. Use IT for information retrieval and presentation. 8. Manage own work |
Indicative Reading List
This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.
G King, R Keohane and S Verba, Designing Social Inquiry (Princeton UP, 1994);
D Burton (ed), Research Training for Social Scientists: A Handbook for Postgraduate Researchers (Sage, 2000).
S. Jackman. Bayesian Analysis for the Social Sciences (Wiley, 2000).
W. Greene. Econometric Analysis (Pearson, 2012).
Angrist, J., and Pishke, S. Mostly Harmless Econometrics (Princeton University Press, 2009).
Gelman, Andrew, and Jennifer Hill. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, 2006.
Kosuke, I.. Quantitative Social Science: An Introduction. (Princeton University Press, 2017)
Wooldridge, J. Econometric Analysis of Cross-Section and Panel Data (2010, MIT University Press).
Subject-specific reading will varying according to research topic.