Unit name | Advanced Quantitative Methods for Social and Policy Research |
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Unit code | POLIM0021 |
Credit points | 20 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
Unit director | Dr. Mircea Popa |
Open unit status | Not open |
Pre-requisites |
EITHER: SPAI20013, SPAI20014 and one of SPAI30013, SPAI30014, SPOL30031 or SPOL30032 OR: |
Co-requisites |
None |
School/department | School of Sociology, Politics and International Studies |
Faculty | Faculty of Social Sciences and Law |
The purpose of this unit is to introduce students to some of the higher-level quantitative methods, concepts and thinking that can be found in contemporary quantitative social science, with application to social and policy research, and taught be drawing upon the lecturers' own experiences of using the methods in their own research. Such topics may include discrete dependent variables, nonparametric estimation, postestimation, time-series, social network analysis, data reduction and reliability, and sampling.
The unit aims:
At the end of this unit a successful student will:
-Be able to employ advanced statistical methods such as maximum likelihood estimation, time series estimation, and social network analysis to analyse social science data. -Be able to use statistical software such as R and Stata to implement the methods taught in the unit. -Be able to address data challenges such as missing data, data reduction and reliability, and sampling concerns. -Be able to engage with current applied research using the methods taught in the unit.Combination of lectures and computer-based seminars.
Portfolio of applied data analysis (100%), assesses all learning outcomes. Students will have a choice among several data analysis methods to use in the assignment, reflecting the methods taught in the unit. Assignment length: 3500-4000 words. Students will receive written comments on their work from the markers.
Guided reading will be given in class. Such reading may include:
Ulrich Kohler, Kristian Bernt Karlson, and Anders Holm, 'Comparing coefficients of nested nonlinear probability models', The Stata Journal, 11/3 (2011), pp. 420-438.