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2023/24 Undergraduate Module Catalogue

PIED3704 Advanced Statistical Analysis

20 creditsClass Size: 12

Module manager: Prof Jocelyn Evans

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24

Pre-requisite qualifications

PIED2711 Analysing Data in Politics, Development and International Relations
LUBS 2570 Introduction to Econometrics

This module is not approved as a discovery module

Module summary

This module builds on the foundation knowledge acquiring in PIED2711 and develops your understanding of multivariate statistical techniques. It will teach the main modelling approaches used in social sciences, as well as the tools to present these analyses as clearly and comprehensibly as possible. The first half of the module focuses on techniques useful for the dissertation, and demonstrates these in workshops; the second half introduces more advanced techniques which you may encounter in research literature or in further study, and dedicates workshop time to preparing and analysing data for the dissertation.


The module will introduce students to advanced statistical models applied in the social sciences, and develop skills from PIED2711 in the preparation and analysis of numerical data. The module will focus on the use of multivariate regression models for different types of outcome, and consolidate knowledge and application of the Stata package in implementing these tests.

Learning outcomes
By the end of the module, students will a) understand the bases to different statistical tests based on functional form; b) understand more complex specifications of independent variables; c) be familiar with simple applications of multi-level models and longitudinal data; d) be able to implement common multivariate techniques to large-scale data using the Stata package; d) be capable of commenting on and explaining other researchers’ statistical models; e) have an understanding of applied analysis which can be used in a range of employment sectors.

Skills outcomes
Applied statistical analysis
Data analysis


The module will cover the following key topics:
- Multivariate regression - a refresher
- Diagnostics
- Interaction effects and graphing probabilities
- Factor analysis and scaling
- Discrete choice models - logit and probit
- Panel and time-series data
- Introduction to multi-level models
- Introduction to path analysis

Teaching methods

Delivery typeNumberLength hoursStudent hours
Independent online learning hours156.00
Private study hours0.00
Total Contact hours33.00
Total hours (100hr per 10 credits)189.00

Private study

Students will use private study time to identify, clean and analyse datasets, for class assessments and, in the second half of the module, in working on their datasets for use in their final-year dissertation. They will also need to engage in reading across the reading to consolidate their knowledge of topics addressed in lectures.

Opportunities for Formative Feedback

Workshops will set students tasks to complete based upon the lecture content (in the first half of the module) and their own datasets intended for analysis in their dissertation (in the second half of the module). The mid-term assignments will provide in-depth feedback which will feed in to their final assignment analysis.

Methods of assessment

Assessment typeNotes% of formal assessment
EssayFinal primary anaylysis (end of term)50.00
PracticalReplication of data analysis (mid-term)35.00
PracticalInterpretation of secondry analysis (mid-term)15.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 15/09/2023


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