Advanced Quantitative Political Analysis

The details
Colchester Campus
Full Year
Undergraduate: Level 6
Thursday 08 October 2020
Friday 02 July 2021
11 May 2020


Requisites for this module



Key module for

BSC LL14 Economics and Politics (Including Foundation Year),
BSC LL2F Economics and Politics,
BSC LL3F Economics and Politics (Including Year Abroad),
BSC LL4F Economics and Politics (Including Placement Year),
BSC L222 Politics and International Relations,
BSC L223 Politics and International Relations (Including Year Abroad),
BSC L224 Politics and International Relations (Including Placement Year)

Module description

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.

Module aims

The aims of the module are to:

1. understand the statistical ideas underpinning quantitative methods in political science research.
2. 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

Module learning outcomes

On successful completion of the module, students should have:

1. Advanced knowledge of descriptive and inferential statistics.
2. Knowledge required to understand the assumptions underlying a broad range of basic and advanced statistical models used in social sciences.
3. Understanding how advanced 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.

Module information

No additional information available.

Learning and teaching methods

The module will be delivered by a (i) weekly pre-recorded lecture and (ii) a weekly interactive lecture. The pre-recorded lecture will consist of one or more items of prepared content that students can access electronically and must study before the interactive lecture. The interactive lecture will consist of one 50-minute lecture in which students can ask questions about, and discuss various aspects of, the prepared content with the module supervisor. Additionally, the interactive lecture will cover programming in R relevant for the problem sets due next. 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 Weighting
Coursework   Problem Set 1    12.5% 
Coursework   Problem Set 2    12.5% 
Coursework   Problem Set 3    12.5% 
Coursework   Problem Set 4    12.5% 
Coursework   Problem set 5    12.5% 
Coursework   Problem Set 6    12.5% 
Coursework   Problem set 7    12.5% 
Coursework   Problem set 8    12.5% 
Exam  180 minutes during Summer (Main Period) (Main) 

Overall assessment

Coursework Exam
60% 40%


Coursework Exam
60% 40%
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



External examiner

Dr Mohammed Rodwan Abouharb
University College London
Available via Moodle
Of 132 hours, 40 (30.3%) hours available to students:
92 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.

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