MDD2QTA2-Introduction to Quantitative Techniques

Module Provider: Marketing and Reputation
Number of credits: 15 [7.5 ECTS credits]
Level:NA
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites: MDD2RDM2 Introduction to Research Design and Methodology and MDD2QLA2 Introduction to Qualitative Techniques
Modules excluded:
Current from: 2022/3

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


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

Additional outcomes:

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 techniques such as multiple regression and factor analyses.

Global context:

The context of the research may be global in nature, therefore, cultural issues will be highlighted to be taken into account when collecting, analysing and interpreting data.

Brief description of teaching and learning methods:

Teaching will involve a combination of lectures, group seminars, practical experiential learning and individual activities in the form of guided self-study. Pre-workshop briefings will give guidance as to the preparatory readings and exercises required to get the best from the teaching.

Summative assessment- Examinations:

None

Summative assessment- Coursework and in-class tests:

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

Formative assessment methods:

N/A

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:

One re-submission is permitted for failed assignments (capped at 50%) 

Additional Costs (specified where applicable):

Last updated: 22 September 2022