GV950-7-SP-CO:
Introduction to Quantitative Methods and Data Analysis II

The details
2023/24
Government
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 15 January 2024
Friday 22 March 2024
15
05 June 2023

 

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

 

(none)

Key module for

MSC L25212 Conflict Resolution,
MSC L252EB Conflict Resolution,
MSC L252EK Conflict Resolution,
MSC L24012 Global and Comparative Politics,
MSC L240EB Global and Comparative Politics,
MSC L240EK Global and Comparative Politics,
MRESL25024 International Relations,
MSC L25012 International Relations,
MSC L250EB International Relations,
MSC L250EK International Relations,
MSC L20612 Political Economy,
MSC L206EB Political Economy,
MSC L206EK Political Economy,
MSC L20012 Political Science,
MSC L200EB Political Science,
MSC L20712 Public Opinion and Political Behaviour,
MSC L207EB Public Opinion and Political Behaviour,
MSC L207EK Public Opinion and Political Behaviour,
MRESL20024 Political Science,
MSC F7D412 Environmental Futures with Climate Change,
MSC L2P312 Politics, Communications and Data Analytics,
MSC L20812 Political Psychology,
MPHDL20148 Government,
PHD L20148 Government

Module description

This module builds on knowledge that students have gained in GV900 and starts with a refresher on linear regression before delving into more advanced methods, both theoretically and practically. We will start with the assumptions that go into linear regressions, learn to analyse whether each assumption holds and consider how our results are impacted when they do not. We will focus on this both from a theoretical standpoint and learn what diagnostic tests can be used in R to test our regression assumptions. For linear regressions, we will also discuss interaction terms and fixed effects estimation.


The remainder of the module will focus on more advanced statistical methods with a focus on two broad categories. The first will be models for analysing non-continuous dependent variables, including binary, categorical and ordinal dependent variables. As before, we will also focus on how to analyse such data in R.


We will also spend some time introducing students to causal inference methods and analysis, using both observational and experimental data.


Along with the various new statistical techniques that students will learn, they will continue to build their coding skills in R throughout the module. In addition to implementing the statistical models, they will learn more advanced coding skills including writing their own (simple) functions, writing for-loops and nested for-loops, and they will learn to work with messy data so that they gain practical skills that are useful for both academic and non-academic spheres.

Module aims

The aims of this module are to:



  1. Ground students in the language of advanced quantitative research including introductory experimental methods;

  2. Equip students with knowledge of a range of advanced statistical techniques including causal inference analyses;

  3. Develop students’ ability to present any data in a meaningful and effective manner;

  4. Develop students’ ability to comment critically on their own and others’ analyses;

  5. Provide training in the advanced use of R;

  6. Show students how to build their own datasets.

Module learning outcomes

By the end of this module, students should be able to:



  1. Build their own datasets from various sources;

  2. Read, understand, and evaluate advanced quantitative analyses published in leading journals;

  3. Manipulate data using advanced techniques in R, including writing for-loops and basic functions;

  4. Analyse data in R using advanced statistical methods suitable for non-continuous dependent variables;

  5. Embark on the more advanced training available to postgraduate students at Essex.

Module information

No additional information available.

Learning and teaching methods

This module will be taught over 2 hours per week

Bibliography

This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   In-class quiz 1    7.5% 
Coursework   In-class quiz 2    7.5% 
Coursework   In-class quiz 3    7.5% 
Coursework   In-class quiz 4    7.5% 
Coursework   Homework 1  07/02/2024  20% 
Coursework   Homework 2  06/03/2024  20% 
Coursework   Homework 3  03/04/2024  30% 

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

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Rabia Malik, email: rabia.malik@essex.ac.uk.

 

Availability
No
No
No

External examiner

No external examiner information available for this module.
Resources
Available via Moodle
Of 20 hours, 10 (50%) hours available to students:
10 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.

 

Further information
Government

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