GV300-6-FY-CO:
Quantitative Political Analysis
2019/20
Government
Colchester Campus
Full Year
Undergraduate: Level 6
Current
Thursday 03 October 2019
Friday 26 June 2020
30
15 May 2019
Requisites for this module
GV207
(none)
(none)
(none)
(none)
BSC L222 Politics and International Relations,
BSC L223 Politics and International Relations (Including Year Abroad),
BSC L224 Politics and International Relations (Including Placement Year)
This module examines quantitative methods in political research and shows how different methods can be used to answer substantive questions about political phenomena. After an initial examination of some tools for statistical inference the rest of the rest term of this module focuses particularly on regression analysis.
Attention is paid to the potential problems of the classical regression model and solutions to these problems. In the second term, we focus on how to use these tools to answer substantive questions about politics. We pay specific attention to threats to causal inference and how research can be designed to overcome them.
1. understand the statistical ideas underpinning quantitative methods in political science research.
2. understand and evaluate the core assumptions of the classical regression model.
3. understand the consequences for statistical inference when these assumptions are violated and to correct these violations in order to make valid inferences.
4. explore issues of causal inference.
5. understand how research design allows causal questions to be answered.
6. employ a variety of functionality of standard statistical software (Stata or R) in their research.
1. Basic knowledge of descriptive and inferential statistics.
2. Knowledge required to understand the assumptions underlying the most common statistical models used in social sciences.
3. Understanding how statistical techniques can be used to answer substantive research questions in political science.
4. Foundations for undertaking work involving the statistical modelling of political phenomena and the study of causal mechanism thereof.
5. Key skills required for research employed in various professional settings.
No additional information available.
1 x 1 hour lecture
1 x 1 hour lab
- Blair, Graeme; Imai, Kosuke; Lyall, Jason. (2014) 'Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan', in American Journal of Political Science. vol. 58 (4) , pp.1043-1063
- Morgan, Stephen L.; Winship, Christopher. (2015) Counterfactuals and causal inference: methods and principles for social research, New York, NY: Cambridge University Press.
- Wooldridge, Jeffrey M. (2018) Introductory econometrics: a modern approach, Boston, MA: Cengage.
- Pettersson-Lidbom, Per. (2008) 'Do Parties Matter for Economic Outcomes? A Regression-Discontinuity Approach', in Journal of the European Economic Association. vol. 6 (5) , pp.1037-1056
- (c1993) 'On behalf of an experimental political science', in Experimental foundations of political science, Ann Arbor: University of Michigan Press. vol. Michigan studies in political analysis
- Lazer, D. (2014) The Parable of Google Flu: Traps in Big Data Analysis.
- Morton, Rebecca B.; Williams, Kenneth C. (2010) Experimental political science and the study of causality: from nature to the lab, Cambridge: Cambridge University Press.
- Gill, Jeff. (2006) Essential mathematics for political and social research, Cambridge: Cambridge University Press.
- Wooldridge, Jeffrey M. (©2020) Introductory econometrics: a modern approach, Boston, MA: Cengage.
- Angrist, Joshua David; Pischke, Jorn-Steffen. (2014) Mastering 'Metrics, New Jersey: Princeton University Press.
- Ludwig, Jens; Miller, Douglas L. (2007) 'Does Head Start Improve Children's Life Chances? Evidence from a Regression Discontinuity Design', in The Quarterly Journal of Economics. vol. 122 (1) , pp.159-208
- Nickerson, David W. (2008) 'Is Voting Contagious? Evidence from Two Field Experiments', in The American Political Science Review. vol. 102 (1) , pp.49-57
- Chen, Min; Mao, Shiwen; Zhang, Yin; Leung, Victor Chung Ming. (2014) Big data: related technologies, challenges and future prospects, Cham: Springer. vol. SpringerBriefs in computer science
- Kellstedt, Paul M.; Whitten, Guy D. (2013) The fundamentals of political science research, Cambridge: Cambridge University Press.
- Gerber, Alan S.; Green, Donald P. (©2012) Field experiments: design, analysis, and interpretation, New York: W.W. Norton.
- Kellstedt, Paul M.; Whitten, Guy D. (2018) The fundamentals of political science research, Cambridge: Cambridge University Press.
- Gelman, Andrew; Hill, Jennifer. (2007) Data analysis using regression and multilevel/hierarchical models, Cambridge: Cambridge University Press. vol. Analytical methods for social research
- Miguel, Edward; Satyanath, Shanker; Sergenti, Ernest. (2004-08) 'Economic Shocks and Civil Conflict: An Instrumental Variables Approach', in Journal of Political Economy. vol. 112 (4) , pp.725-753
- Kellstedt, Paul M.; Whitten, Guy D. (2018) The fundamentals of political science research, New York: Cambridge University Press.
- Gaines, Brian J.; Kuklinski, James H.; Quirk, Paul J. (2007) 'The Logic of the Survey Experiment Reexamined', in Political Analysis. vol. 15 (01) , pp.1-20
The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.
Assessment items, weightings and deadlines
Coursework / exam |
Description |
Deadline |
Coursework weighting |
Coursework |
Problem Set 1 |
23/10/2019 |
8.337% |
Coursework |
Problem Set 2 |
06/11/2019 |
8.337% |
Coursework |
Problem Set 3 |
20/11/2019 |
8.337% |
Coursework |
Problem Set 4 |
04/12/2019 |
8.337% |
Coursework |
Problem Set 5 |
05/02/2020 |
8.337% |
Coursework |
Problem set 6 |
19/02/2020 |
8.337% |
Coursework |
Problem set 7 |
04/03/2020 |
8.337% |
Coursework |
Problem Set 8 |
25/03/2020 |
8.337% |
Written Exam |
Mid-Term test |
|
33.3% |
Exam |
Main exam: 24hr during Summer (Main Period)
|
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
Dr Dominik Duell, email: dominik.duell@essex.ac.uk.
Dr Dominik Duell
Module Supervisor: Dr Duell, email dominik.duell @essex.ac.uk
Module Administrator: Sallyann West, govquery@essex.ac.uk
Yes
Yes
No
Dr Mohammed Rodwan Abouharb
University College London
Available via Moodle
Of 166 hours, 18 (10.8%) hours available to students:
148 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
Government
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