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2022/23 Taught Postgraduate Module Catalogue

LUBS5283M Advanced Quantitative Methods

15 creditsClass Size: 50

Module manager: Shankha Basu

Taught: 1 Jun to 31 Jul View Timetable

Year running 2022/23

Pre-requisite qualifications

All students participating in this module must already have obtained the required qualifications to enter the LUBS PhD programme. LUBS 5285M or equivelent knowledge of quantitative methods.


LUBS5285MIntroduction to Research Design and Data Analysis

This module is not approved as an Elective

Module summary

This module is a part of the LUBS PhD training programme. It builds on the basic exposure to quantitative methods provided in the introduction to research design and data analysis. The primary aim of the course will be to introduce you to contemporary advanced quantitative analysis techniques and to provide guidelines on how you can use these skills in your doctoral works and journal publications. The module is optional since you can choose to attend Advanced Quantitative Methods or Advanced Qualitative Methods. It is scheduled in semester two over four days.The module assessment will be based on a draft paper which must include all analysis, table, figures and description of the results. Students will choose their own topics that apply or develop appropriate statistical models to an important substantive problem. The draft paper makes up 100% of the marks for this module.


This module aims to build on the introductory training in quantitative methods provided in Introduction to Research Design and Data Analysis (LUBS 5285M), and to provide coverage of more advanced and contemporary quantitative approaches, focusing on developing students’ multivariate statistical analysis techniques: linear/mediation/moderation regression analyses; and structural equation modelling.

Learning outcomes
Upon completion of this module students will be able to critically assess:
- the underlying theory and application of the methods studied
- use of appropriate software packages to analyse quantitative datasets

Skills outcomes
Upon completion of this module students will be able to:
Subject Specific
- Conduct complex statistical analyses for their PhD dissertations and academic papers
- Present results of statistical analyses in conferences and in journal papers

- Work effectively as an analyst
- Analyse and present reasoned and logical conclusions from a set of data
- Prepare and deliver an academic presentations


Indicative content
- Factor analysis: exploratory factor analysis vs. confirm factor analysis
- Introduction to structural equation modelling
- Introduction to hierarchical models
- Basic text analysis
- Experiment design
- Moderation, mediation, and conditional processes

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours125.00
Total Contact hours25.00
Total hours (100hr per 10 credits)150.00

Private study

This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.

Opportunities for Formative Feedback

Your teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.

Methods of assessment

Assessment typeNotes% of formal assessment
Assignment3,000 words100.00
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: 30/01/2023


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