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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,Ìý 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.
Ìý | Autumn | Spring | Summer |
Lectures | 44 | ||
Guided independent study: | Ìý | Ìý | Ìý |
Ìý Ìý Wider reading (independent) | 10 | ||
Ìý Ìý Wider reading (directed) | 10 | ||
Ìý Ìý Advance preparation for classes | 10 | ||
Ìý Ìý Completion of formative assessment tasks | 40 | ||
Ìý Ìý Essay preparation | 30 | ||
Ìý Ìý Reflection | 6 | ||
Ìý | Ìý | Ìý | Ìý |
Total hours by term | 150 | 0 | 0 |
Ìý | Ìý | Ìý | Ìý |
Total hours for module | 150 |
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).Ìý
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
THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.