Timing and CATS
This module will run in the Autumn Term (weeks 2-10) and is worth 15 CATS.
**Module currently under review, changes expected for 2018/19.**
In the age of ever-increasing data availability which is paired with a growing sophistication of statistical techniques, the opportunities for social science research are vast. This module will give you an understanding of the basic elements of core descriptive and inferential statistics which will allow you not only to critically engage with quantitative findings in existing social science research, but also conduct quantitative analysis yourself. The module covers the topics of conceptualisation, operationalisation and measurement, as well as the principles of sampling and the basics of statistical inference. You will be introduced to the statistical methods and process of social science research in one hour lectures and then, and then explore these in extended seminars (2h) both through readings, and the statistical software STATA. We will be working on real data sets, such as the World Development Indicators, but you will also conduct your own little survey amongst other students and analyse the data in class afterwards. Great emphasis of this module rests on demonstrating the relevance of the methods in substantive areas of political science and sociology. This means, for example, that I do not just want to talk about conceptualisation as an abstract exercise, but I will give you plenty of examples, such as "national identity", "climate change", and "social capital", to show that how we understand and measure a particular concept does matter. Throughout the module, you will have the opportunity to test your knowledge every week with non-assessed quizzes on moodle. The assessment proper proceeds in two parts, one report of 750 words due in week 5 (worth 15%), and a final summative report of 2,000 words (worth 85%). In these reports you will explore and critically reflect on the conceptualisation, measurement and descriptive statistics of a social science concept.