MDD2QTA1-Introduction to Quantitative Techniques

Module Provider: Marketing and Reputation
Number of credits: 15 [7.5 ECTS credits]
Terms in which taught: Autumn term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Prof Carola Hillenbrand

Type of module:

Summary module description:

This module seeks to develop understanding of some key methods and techniques in quantitative data analysis and to introduce software for quantitative data analysis.


The module aims to enable programme members to:

  • Develop their understanding of some of the main methods and techniques of quantitative data analysis

  • Develop competence in interpreting findings

  • Develop practical skills in using software for quantitative data analysis

Assessable learning outcomes:

By the end of the module it is expected that programme members will be able to demonstrate their ability to:

  • Select with justification appropriate methods to analyse given data

  • Use methods in an appropriate way with an understanding of the assumptions of a particular method

  • Evaluate and interpret results, recognising any limitations

  • Report findings in a clear, concise and well-structure manner

  • Dem onstrate competence in the use of appropriate software for quantitative data analysis

Additional outcomes:

By the end of the module it is expected that programme members will be able to demonstrate their ability to:

  • Communicate clearly and confidently about research issues in both oral and written communication

  • Work autonomously, as well as collaboratively,  managing their process of study, prioritising appropriately

  • Manage the research process to gather required information and data with minimum of guidance

  • Reflect on their own understanding and ability to communicate with others in the subject area

Outline content:

The module content includes introduction to quantitative data analysis, basic statistical concepts, exploration of research design and measurement, issues of questionnaire design and data collection and introduction to a number of multivariate statistical technics such as multiple regression and factor analyses.

Brief description of teaching and learning methods:

The module teaching is structured around 6 workshop days, which involve a combination of lectures, group and individual activities. In addition, programme members are expected to undertake independent self-study.

Contact hours:
  Autumn Spring Summer
Lectures 44
Guided independent study:      
    Wider reading (independent) 10
    Wider reading (directed) 10
    Advance preparation for classes 10
    Completion of formative assessment tasks 40
    Essay preparation 30
    Reflection 6
Total hours by term 150 0 0
Total hours for module 150

Summative Assessment Methods:
Method Percentage
Written assignment including essay 100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

3,000-word assignment (including text in tables) (+20% / -10%)

Formative assessment methods:

Penalties for late submission:

- Up to 30 days late (with no extension requested) – 10 mark reduction and only one re-submission permitted

- More than 30 days late (with no extension requested) – 0 mark applied and only one re-submission permitted

Assessment requirements for a pass:

A percentage mark is given (50-59% pass, 60-69% merit, >70% distinction). 

Reassessment arrangements:

The assignment may be resubmitted once (capped at 60%).

Additional Costs (specified where applicable):

Travel to, and attendance at 6-day workshop (may require accommodation/subsistence)

Last updated: 23 June 2021


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