IS40730 Quantitative Data Analysis

Academic Year 2022/2023

The use of quantitative data to explore hypotheses about the world is a cornerstone of scientific enquiry. We are now living in a world with an unprecedented volume of datasets which can be used when making real world decisions and predictions. The understanding of how to collect, analyse and interpret quantitative data is a core skill for information professionals in the data driven age. The course will give students a background into the processes and concepts related to collecting, analysing and interpreting quantitative data. The course is fundamentally practical in nature and will focus on introducing students to the R statistical programming language and R studio as well as the concepts important in data analysis and statistics. The course is aimed at students with no previous programming, mathematics or statistical analysis experience. The lectures are structured as a 1hr lecture with an additional 50 minute practical, where students will be guided step by step through analysis of datasets in R.Core Course TextField, A., Miles, J., & Field, Z. (2012). Discovering Statistics using R. Sage Publications

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Curricular information is subject to change

Learning Outcomes:

Learning OutcomesOn successful completion of the module students should be able to: • Understand the benefits and drawbacks of the scientific method and quantitative data approaches • Recognise and explain the appropriateness of statistical test for data analysis • Execute a number of common statistical tests in R • Interpret findings from these tests accurately • Report findings from quantitative data appropriately and accurately

Indicative Module Content:

Indicative content (note this is subject to change)

Lecture 1- What is quantitative data?
Lecture 2- Experiment design and hypothesis development

Lecture 3- Concepts in stats: Means, Deviation and graphs

Lecture 4- Correlations: Positive or negative relationships
Lecture 5- Regression- the key to (most) statistics

Lecture 6- Multiple regression- modelling complexity
Lecture 7- T-test- The story of stout and stats

Lecture 8- ANOVA: comparing three groups

Lecture 9- Data analysis session/revision

Lecture 10- Online Quiz

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning




Computer Aided Lab




Approaches to Teaching and Learning:
Approaches used in this module include:

Lectures; Critical writing & reflective learning; Case-based/Project based learning 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
IS30330 - Quantitative Data Analysis

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: Class Test Week 10 n/a Alternative linear conversion grade scale 40% No


Assignment: Report 2 Week 12 n/a Standard conversion grade scale 40% No


Assignment: Report 1 Week 6 n/a Standard conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Reports 1 & 2: Written feedback delivered via Brightspace post assessment Class Test: Online automated feedback given

Field, Miles, & Field (2012). Discovering SPSS Using R. Sage Publication
Other sources will be identified for each week.
Name Role
Justin Edwards Lecturer / Co-Lecturer
Paola Peña Lecturer / Co-Lecturer
Yunhan Wu Tutor