2024/25 Taught Postgraduate Module Catalogue
LUBS5283M Advanced Quantitative Methods
15 creditsClass Size: 50
Module manager: Bulent Menguc
Email: B.Menguc@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
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 equivalent knowledge of quantitative methods.Pre-requisites
LUBS5285M | Introduction 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. 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.Objectives
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
Transferable
- Work effectively as an analyst
- Analyse and present reasoned and logical conclusions from a set of data
- Prepare and deliver an academic presentations
Syllabus
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 type | Number | Length hours | Student hours |
Workshop | 5 | 5.00 | 25.00 |
Private study hours | 125.00 | ||
Total Contact hours | 25.00 | ||
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
Coursework
Assessment type | Notes | % of formal assessment |
Assignment | 3,000 words | 100.00 |
Total percentage (Assessment Coursework) | 100.00 |
The resit for this module will be assessed 100% by 3,000 word assignment.
Reading list
The reading list is available from the Library websiteLast updated: 16/08/2024 11:44:41
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