MDD2QTA1-Introduction to Quantitative Techniques

Module Provider: Marketing and Reputation
Number of credits: 15 [7.5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Prof Carola Hillenbrand
Email: carola.hillenbrand@henley.ac.uk

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.

Aims:

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.

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