Advanced Quantitative Political Analysis

The details
Colchester Campus
Full Year
Undergraduate: Level 6
Thursday 07 October 2021
Friday 01 July 2022
01 September 2021


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 LL25 Politics with Business,
BSC LL26 Politics with Business,
BSC LL27 Politics with Business (including Year Abroad),
BSC LL20 Politics with Data Science,
BSC LL21 Politics with Data Science,
BSC LL22 Politics with Data Science

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

1x Weekly pre-recorded Lecture 1x Weekly interactive Lecture


  • Kellstedt, Paul M.; Whitten, Guy D. (2018) The fundamentals of political science research, New York: Cambridge University Press.
  • Wooldridge, Jeffrey M. (©2020) Introductory econometrics: a modern approach, Boston, MA: Cengage.
  • 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
  • Kellstedt, Paul M.; Whitten, Guy D. (2013) The fundamentals of political science research, Cambridge: Cambridge University Press.
  • Morgan, Stephen L.; Winship, Christopher. (2015) Counterfactuals and causal inference: methods and principles for social research, New York, NY: Cambridge University Press.
  • Angrist, Joshua David; Pischke, Jörn-Steffen. (2015) Mastering 'metrics: the path from cause to effect, Princeton: Princeton University Press.
  • NICKERSON, DAVID W. (2008-02) 'Is Voting Contagious? Evidence from Two Field Experiments', in American Political Science Review. vol. 102 (1) , pp.49-57
  • Gill, Jeff. (2006) Essential mathematics for political and social research, Cambridge: Cambridge University Press.
  • Greene, William H. (©2018, 2012, 2008) Econometric analysis, New York, NY: Pearson.
  • Gelman, Andrew; Hill, Jennifer. (2007) Data analysis using regression and multilevel/hierarchical models, Cambridge: Cambridge University Press. vol. Analytical methods for social research
  • Morton, Rebecca B.; Williams, Kenneth C.; EBSCOhost ebook collection. (2010) Experimental political science and the study of causality: from nature to the lab, Cambridge: Cambridge University Press.
  • Wooldridge, Jeffrey M. (2018) Introductory econometrics: a modern approach, Boston, MA: Cengage.
  • Gelman, Andrew; Hill, Jennifer. (2007) Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge: Cambridge University Press.
  • 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
  • Gerber, Alan S.; Green, Donald P. (2012) Field Experiments, New York: WW Norton & Co.
  • Kaplan, Daniel T. (2017) Statistical Modeling: A Fresh Approach: Project MOSAIC Books.
  • Dunning, Thad. (c2012) Natural experiments in the social sciences: a design-based approach, Cambridge: Cambridge University Press.
  • Blair, Graeme; Imai, Kosuke; Lyall, Jason. (1043) 'Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan', in Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan. vol. 58 (4) , pp.1043-1063
  • 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
  • Kellstedt, Paul; Whitten, Guy. (2013) The Fundamentals of Political Science Research, Cambridge: Cambridge University Press.
  • Gerber, Alan; Green, Donald; Kaplan, Edward. (no date) The Illusion of Learning from Observational 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

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   Homework 1  09/11/2021  15% 
Coursework   Homework 2  30/11/2021  15% 
Coursework   Homework 3  25/01/2022  20% 
Coursework   Homework 4  15/02/2022  15% 
Coursework   Homework 5  15/03/2022  15% 
Coursework   Homework 6  29/03/2022  20% 

Overall assessment

Coursework Exam
100% 0%


Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Ryan Bakker, email: r.bakker@essex.ac.uk.
Module Supervisor: Ryan Bakker Module Administrator: Edmund Walker, govquery@essex.ac.uk



External examiner

Dr Mohammed Rodwan Abouharb
University College London
Available via Moodle
Of 1478 hours, 40 (2.7%) hours available to students:
1438 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

Disclaimer: The University makes every effort to ensure that this information on its Module Directory is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to programmes, modules, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of key personnel, change in government policy, or withdrawal/reduction of funding. Changes to modules may for example consist of variations to the content and method of delivery or assessment of modules and other services, to discontinue modules and other services and to merge or combine modules. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications and module directory.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.