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MDD2QTA2 - Introduction to Quantitative Techniques

MDD2QTA2-Introduction to Quantitative Techniques

Module Provider: Henley Business School
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: 2023/4

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.


Contact hours:
  Autumn Spring Summer
Lectures 48
Guided independent study:      
    Wider reading (independent) 10
    Wider reading (directed) 30
    Advance preparation for classes 20
    Essay preparation 30
    Reflection 12
       
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:

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):









Travel, accommodation, and subsistence



Travel to, and attendance at a workshop (may require accommodation/subsistence)



Last updated: 12 October 2023

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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