GV900-7-FY-CO:
Political Explanation

The details
2020/21
Government
Colchester Campus
Full Year
Postgraduate: Level 7
Current
Thursday 08 October 2020
Friday 02 July 2021
30
05 June 2020

 

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

 

GV903, GV906, GV916

Key module for

MA L25212 Conflict Resolution,
MA L252EB Conflict Resolution,
MA L252EK Conflict Resolution,
MA L24012 Global and Comparative Politics,
MA L240EB Global and Comparative Politics,
MA L240EK Global and Comparative Politics,
MA L25012 International Relations,
MA L250EB International Relations,
MA L250EK International Relations,
MRESL25024 International Relations,
MA L20612 Political Economy,
MA L206EB Political Economy,
MA L206EK Political Economy,
MA L20012 Political Science,
MA L200EB Political Science,
MA L20712 Public Opinion and Political Behaviour,
MA L207EB Public Opinion and Political Behaviour,
MA L207EK Public Opinion and Political Behaviour,
MRESL20024 Political Science,
MA L24512 United States Politics,
MA F7D412 Environmental Futures with Climate Change,
MPOLL268 International Relations,
MPOLL269 International Relations (Including Placement Year),
MPOLL370 International Relations (Including Year Abroad),
MPOLL234 Politics and International Relations,
MPOLL235 Politics and International Relations (Including Placement Year),
MPOLL236 Politics and International Relations (Including Year Abroad)

Module description

This module offers an introduction to the theory and practice of quantitative data analysis techniques. The goals are to provide students with the skills that are necessary to: 1) read, understand, and evaluate the academic literature, and 2) design and carry out studies that employ these techniques for testing substantive theories.

The module serves three principal purposes.

The first is to ground students in the language of social science research: research questions, independent and dependent variables, hypotheses, causality, etc. Students will come across these terms relentlessly in this module, in other modules, and throughout social science. It is thus important that you are able to use them readily and correctly.

The second purpose is to familiarise yourself with the types of data and the practice of data analysis in the social sciences. Students are introduced to a range of sources from which they can access quantitative data. Student will also be introduced to the programming language R, which is widely used by academics and practitioners for the analysis of quantitative data. I will assume that students have no prior experience with any of this software, and so students will be given a full introduction to their use.

The third purpose is to introduce a series of statistical techniques for the analysis of quantitative data. Some of the techniques are fairly simple, while others (especially those covered in the final weeks of the module) are advanced. The good news is that as the work becomes more challenging, the relevance of the techniques to modern social science research becomes more apparent.

Module aims

The purposes of this module are to:
• demonstrate the role of quantitative methods in answering research questions;
• ground students in the language of quantitative research;
• equip students with knowledge of a range of statistical techniques;
• develop students' ability to interpret statistical information in substantive terms;
• develop students' ability to comment critically on their own and others' analyses;
• provide training in the use of the R program;
• show students how to build their own and to locate existing datasets.

Module learning outcomes

By the end of the module students should be able to:
• read, understand, and evaluate quantitative analyses published in the leading journals;
• assess quantitative measurement in terms of reliability and validity;
• understand the correct statistical method for particular research questions and variables;
• use a range of statistical methods, from calculating means to various regression models;
• analyse quantitative data with R;
• build their own datasets, and to download and use existing datasets;
• embark on the more advanced training available to postgraduate students at Essex.

Module information

Module Description

This module offers an introduction to the theory and practice of quantitative data analysis techniques. The goals are to provide students with the skills that are necessary to:

1) read, understand, and evaluate the academic literature, and

2) design and carry out studies that employ these techniques for testing substantive theories.

The module serves three principal purposes.

The first is to ground students in the language of social science research: research questions, independent and dependent variables, hypotheses, causality, etc. Students will come across these terms relentlessly in this module, in other modules, and throughout social science. It is thus important that you are able to use them readily and correctly.

The second purpose is to familiarise yourself with the types of data and the practice of data analysis in the social sciences. Students are introduced to a range of sources from which they can access quantitative data. Student will also be introduced to the programming language R, which is widely used by academics and practitioners for the analysis of quantitative data. I will assume that students have no prior experience with any of this software, and so students will be given a full introduction to their use.

The third purpose is to introduce a series of statistical techniques for the analysis of quantitative data. Some of the techniques are fairly simple, while others (especially those covered in the final weeks of the module) are advanced. The good news is that as the work becomes more challenging, the relevance of the techniques to modern social science research becomes more apparent.

Aims:
The purposes of this module are to:
* demonstrate the role of quantitative methods in answering research questions;
* ground students in the language of quantitative research;
* equip students with knowledge of a range of statistical techniques;
* develop students' ability to interpret statistical information in substantive terms;
* develop students' ability to comment critically on their own and others' analyses;
* provide training in the use of the R program;
* show students how to build their own and to locate existing datasets.

Learning Outcomes:
By the end of the module students should be able to:
* read, understand, and evaluate quantitative analyses published in the leading journals;
* assess quantitative measurement in terms of reliability and validity;
* understand the correct statistical method for particular research questions and variables;
* use a range of statistical methods, from calculating means to various regression models;
* analyse quantitative data with R;
* build their own datasets, and to download and use existing datasets;
* embark on the more advanced training available to postgraduate students at Essex.

Key Skills:
Students will require, use, and develop the following key skills:
* Critical thinking: students will consider the pros and cons of quantitative analysis and be able to evaluate their own and others' research designs, measures and analyses;
* Transfer of ideas: students will be helped to follow and assess quantitative research in other modules: political behaviour, comparative politics, international relations, etc.;
* Use of statistical programs to analyse data: students will learn in lab sessions how to use the statistical programs R to analyse quantitative data;
* Improving independent learning and performance: Students will learn how they might think about and to address their own research topics in a quantitative framework;
* Communication and interaction: Both the lectures and lab sessions involve not only questions from the lecturer but also group discussions;
* Writing: Students learn how to report on and assess results of their quantitative analyses.

Learning and teaching methods

We have 4 hours each week allocated to the teaching of GV900. There will be a lecture session and each student also has to attend a practical session in the computer lab every week. The time and location of your practical session will be on your Online Timetable.

Bibliography*

  • Huth, Paul; Croco, Sarah; Appel, Benjamin. (2012) 'Law and the Use of Force in World Politics: The Varied Effects of Law on the Exercise of Military Power in Territorial Disputes', in International Studies Quarterly. vol. 56 (1) , pp.17-31
  • Kellstedt, Paul M.; Whitten, Guy D. (©2018) The fundamentals of political science research, New York: Cambridge University Press.
  • Buhaug, Halvard; Cederman, Lars-Erik; Gleditsch, Kristian Skrede. (2014-06) 'Square Pegs in Round Holes: Inequalities, Grievances, and Civil War', in International Studies Quarterly. vol. 58 (2) , pp.418-431
  • David B. Carter and Curtis S. Signorino. (2010) 'Back to the Future: Modeling Time Dependence in Binary Data', in Political Analysis: Oxford University Press. vol. 18 (3) , pp.271-292
  • Beck, Nathaniel; Katz, Jonathan N.; Tucker, Richard. (1973-) 'Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable', in American Journal of Political Science. vol. 42 (4) , pp.1260-1288
  • James A Piazza. (2011) 'Poverty, minority economic discrimination, and domestic terrorism', in Journal of Peace Research: Sage Publications, Ltd. vol. 48 (3) , pp.339-353
  • Lesley G. Terris and Zeev Maoz. (2005) 'Rational Mediation: A Theory and a Test', in Journal of Peace Research: Sage Publications, Ltd. vol. 42 (5) , pp.563-583
  • Garrett, Geoffrey; Mitchell, Deborah. (2001-03) 'Globalization, government spending and taxation in the OECD', in European Journal of Political Research. vol. 39 (2) , pp.145-177
  • Michael D Ward, Brian D Greenhill and Kristin M Bakke. (2010) 'The perils of policy by p-value: Predicting civil conflicts', in Journal of Peace Research: Sage Publications, Ltd. vol. 47 (4) , pp.363-375
  • Brambor, Thomas; Clark, William Roberts; Golder, Matt. (1989-) 'Understanding Interaction Models: Improving Empirical Analyses', in Political Analysis: Oxford University Press. vol. 14 (1) , pp.66-82
  • Maindonald, J.H. (2008) Using R for Data Analysis and Graphics: Introduction, Code and Commentary.
  • King, Gary. (1995-09) 'Replication, Replication', in PS: Political Science and Politics. vol. 28 (3) , pp.444-

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   In-Class Test    30% 
Coursework   Homework 1    17.5% 
Coursework   Homework 2    17.5% 
Coursework   Homework 3    17.5% 
Coursework   Homework 4    17.5% 

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Ryan Bakker, email: r.bakker@essex.ac.uk.
Dr Howard Liu, email: howard.liu@essex.ac.uk.
Dr Ryan Bakker, Dr Howard Liu
Module Supervisors Dr Ryan Bakker r.bakker@essex.ac.uk or Dr Howard Liu hl20840@essex.ac.uk Module Administrator Jamie Seakens govpgquery@essex.ac.uk

 

Availability
Yes
No
No

External examiner

Dr Nicholas Walter Vivyan
University of Durham
Senior Lecturer
Resources
Available via Moodle
Of 204 hours, 80 (39.2%) hours available to students:
124 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Government

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

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