GV903-7-FY-CO:
Advanced Research Methods

The details
2019/20
Government
Colchester Campus
Full Year
Postgraduate: Level 7
Current
Thursday 03 October 2019
Friday 26 June 2020
30
30 September 2019

 

Requisites for this module
(none)
(none)
(none)
GV900

 

GV906

Key module for

MA L25212 Conflict Resolution,
MA L252EK Conflict Resolution,
MSC L25212 Conflict Resolution,
MSC L252EK Conflict Resolution,
MA L24012 Global and Comparative Politics,
MA L240EK Global and Comparative Politics,
MSC L24012 Global and Comparative Politics,
MSC L240EK Global and Comparative Politics,
MA L25012 International Relations,
MA L250EK International Relations,
MRESL25024 International Relations,
MSC L25012 International Relations,
MSC L250EK International Relations,
MA L20612 Political Economy,
MA L206EK Political Economy,
MRESL20624 Political Economy,
MSC L20612 Political Economy,
MSC L206EK Political Economy,
MA L20012 Political Science,
MSC L20012 Political Science,
MA L20712 Public Opinion and Political Behaviour,
MA L207EK Public Opinion and Political Behaviour,
MSC L20712 Public Opinion and Political Behaviour,
MSC L207EK Public Opinion and Political Behaviour,
MRESL20024 Political Science,
MA L24512 United States Politics,
MSC L16512 Quantitative International Development

Module description

This module presents quantitative methods essential to test hypotheses. The first part of the course concentrates on hypothesis testing, hypothesis testing using least squares, and some classic violations of the Gauss-Markov conditions. In this first part, we will cover cross-sectional and longitudinal models for continuous dependent variables.

The second part of the module focuses on more advanced models ubiquitous in political science based on maximum likelihood estimation and other estimation techniques, starting with the generalised linear model and its various outcome distributions and ending with advanced topics like inferential network analysis and causal inference.

The models and methods are approached substantively, mathematically, and computationally. We will replicate important results using computer programs. The module makes extensive use of the statistical programming environment R. In addition to the methods and software, we will cover some empirical applications to substantive questions. This is particularly important because students should familiarise themselves with the interpretation and presentation of empirical evidence.

Module aims

Aims:
The aim of the module is to test hypotheses using advanced research methods.

Objectives:
The main goals of the module are to understand a large number of econometric models and to choose the appropriate method to test specific hypotheses.

Module learning outcomes

Learning Outcomes:
Knowledge of larges classes of statistical models and advanced research methods. Proficiency using the statistical software R.

Key Skills:
Mathematics, including algebra, linear algebra, and calculus. Statistics, including probability. Econometrics.

Module information

No additional information available.

Learning and teaching methods

2 hour lecture per week, followed by a 2 hour lab session

Bibliography

  • Beck, Nathaniel. (2008) 'Time-Series Cross-Sectional Methods', in The Oxford handbook of political methodology, Oxford: Oxford University Press. vol. Oxford handbooks of political science
  • Long, J. Scott. (c1997) Regression models for categorical and limited dependent variables, Thousand Oaks: Sage. vol. 7
  • Wooldridge, Jeffrey M. (©2020) Introductory econometrics: a modern approach, Boston, MA: Cengage.
  • Ward, Michael D.; Ahlquist, John S. (2018) Maximum Likelihood for Social Science, Cambridge: Cambridge University Press.
  • King, Gary. (1989) Unifying political methodology: the likelihood theory of statistical inference, Cambridge: Cambridge University Press.
  • Bueno de Mesquita, Bruce. (c2014) Principles of international politics, Los Angeles: Sage/CQ Press.
  • Wackerly, Dennis D.; Mendenhall, William; Scheaffer, Richard L. (2008) Mathematical statistics with applications, Belmont, CA: Thomson Brooks/Cole.
  • Myung, In Jae. (2003-2) 'Tutorial on maximum likelihood estimation', in Journal of Mathematical Psychology. vol. 47 (1) , pp.90-100
  • 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
  • Alvarez, R. Michael; Nagler, Jonathan. (1998-01) 'When Politics and Models Collide: Estimating Models of Multiparty Elections', in American Journal of Political Science. vol. 42 (1) , pp.55-
  • Box-Steffensmeier, Janet M.; Jones, Bradford S. (1997-10) 'Time is of the Essence: Event History Models in Political Science', in American Journal of Political Science. vol. 41 (4) , pp.1414-
  • Box-Steffensmeier, Janet M.; Zorn, Christopher J. W. (2001-10) 'Duration Models and Proportional Hazards in Political Science', in American Journal of Political Science. vol. 45 (4) , pp.972-
  • Reuveny, Rafael; Li, Quan. (2003-09) 'The Joint Democracy-Dyadic Conflict Nexus: A Simultaneous Equations Model', in International Studies Quarterly. vol. 47 (3) , pp.325-346
  • Leifeld, Philip; Cranmer, Skyler J.; Desmarais, Bruce A. (2018) 'Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals', in Journal of Statistical Software. vol. 83 (6)
  • Jones, Bradford S.; Branton, Regina P. (2005-12) 'Beyond Logit and Probit: Cox Duration Models of Single, Repeating, and Competing Events for State Policy Adoption', in State Politics & Policy Quarterly. vol. 5 (4) , pp.420-443
  • Cranmer, Skyler J.; Leifeld, Philip; McClurg, Scott D.; Rolfe, Meredith. (2017-01) 'Navigating the Range of Statistical Tools for Inferential Network Analysis', in American Journal of Political Science. vol. 61 (1) , pp.237-251
  • Sigelman, Lee; Zeng, Langche. (2000) 'Analyzing Censored and Sample-Selected Data with Tobit and Heckit Models', in Political Analysis. vol. 8 (2) , pp.167-182
  • Ho, Daniel E.; Imai, Kosuke; King, Gary; Stuart, Elizabeth A. (2007) 'Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference', in Political Analysis. vol. 15 (3) , pp.199-236
  • Cottrell, Allin. (1995) A Short Introduction to LaTeX.
  • Greene, William H. (©2018, 2012, 2008) Econometric analysis, New York, NY: Pearson.
  • Best, Henning; Wolf, Christof. (c2015) The SAGE handbook of regression analysis and causal inference, London: SAGE.
  • Torfs, Paul; Brauer, Claudia. (2014) A (Very) Short Introduction to R.
  • Kam, Cindy D.; Palmer, Carl L. (2008-07) 'Reconsidering the Effects of Education on Political Participation', in The Journal of Politics. vol. 70 (3) , pp.612-631
  • King, Gary; Tomz, Michael; Wittenberg, Jason. (1973-) 'Making the Most of Statistical Analyses: Improving Interpretation and Presentation', in American journal of political science, Hoboken, NJ [etc.]: Wiley-Blackwell Pub. on behalf of the Midwest Political Science Association [etc.]. vol. 44 (2) , pp.341-355
  • King, Gary. (©1998) Unifying political methodology: the likelihood theory of statistical inference, Ann Arbor: University of Michigan Press.
  • Paolino, Philip. (2001) 'Maximum Likelihood Estimation of Models with Beta-Distributed Dependent Variables', in Political Analysis. vol. 9 (4) , pp.325-346
  • Brambor, Thomas; Roberts Clark, William; Golder, Matt. (no date) 'Understanding Interaction Models: Improving Empirical Analyses', in Political Analysis, Political Analysis. vol. 14 (1) , pp.63-82

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 Assignment 1 12/11/2019 25%
Coursework Assignment 2 14/01/2020 25%
Coursework Assignment 3 04/02/2020 25%
Coursework Assignment 4 17/03/2020 25%

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Professor Philip Leifeld, Dietrich Pena-Zuluaga
Module Supervisor Professor Philip Leifeld philip.leifeld@essex.ac.uk or Module Administrator, Jamie Seakens (govpgquery@essex.ac.uk)

 

Availability
No
No
Yes

External examiner

Dr Nicholas Walter Vivyan
University of Durham
Senior Lecturer
Resources
Available via Moodle
Of 80 hours, 40 (50%) hours available to students:
40 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Government

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.