Gwybodaeth Modiwlau

Module Identifier
Module Title
Quantitative Data Collection and Analysis
Academic Year
Semester 1
Reading List
Other Staff

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Assessment Weekly assignments - online quizzes  50%
Semester Assessment Quantitative Research Report  - 2,000 word report containing array of hypothesis tests  50%
Supplementary Assessment - resubmission of failed components 

Learning Outcomes

On successful completion of this module students should be able to:

1. Demonstrate understanding of how basic test statistics are generated.
2. Evaluate and critique quantitative data study design.
3. Demonstrate understanding of different types of statistical data and hypothesis tests.
4. Formulate suitable hypothesis statements
5. Display mastery of using statistical software to run hypothesis tests.
6. Organise and disseminate results and discussion into a professional level standard report.

Brief description

This module is designed to be an integral component of the RT courses which the University has introduced in order to meet the joint funding Research Councils statement on Research Training. Through this module Masters and PhD students will gain a broad knowledge of a range of transferable skills which they can apply in a variety of research contexts. Students will be introduced to various statistical methods. The taught sessions are a hybrid of theory covered in a lecture style, class discussion, practical computing demonstrations and time for students to assimilate themselves with the relevant software. These sessions will cover basic statistical theory, assumptions underpinning different hypothesis tests and the practical application of software. Additionally students will be exposed to peer-reviewed research papers to familiar themselves with the standard of report writing expected for successful completion of graduate level study. Students will be supported by online material which includes video demonstrations, powerpoints and a bank of examples relevant to a range of disciplines. They are expected to study using this material to then complete relevant assignments.


This module aims to develop student’s knowledge and confidence in identifying appropriate statistical techniques to apply within their graduate studies.
The underlying theory of a range of statistical tests will be delivered and different sampling methods discussed. Students will be instructed in using statistical software to run hypothesis tests and they will be trained in interpreting the results and formulating into professional level documents. Students will be encouraged to analyse data which is applicable to their discipline and profession within their graduate studies.


Types of data
Sample collection methods
Generation of suitable hypothesis statements.
Standard normal distribution

Use of SPSS software: Data entry, summary statistics, splitting of data, plotting of data.

Use of SPSS software to run the following Hypothesis tests:
Student’s t-distribution
Pearson correlation
Linear and multiple regression
Spearman correlation
Chi squared test of goodness of fit
Chi squares test of association
Non-parametric equivalents: Friedman, Kruskal–Wallis, Mann – Whitney test and Wilcoxon Signed-rank Test

Module Skills

Skills Type Skills details
Application of Number
Communication Students will be expected to write up and present statistical results in a professionally formatted document. As part of the delivery of the module they will be expected to communicate with peers their chosen field of research and discuss data collection strategies.
Improving own Learning and Performance The module is taught to students from a diverse range of subjects whom have a wide range of previous mathematical attainment. This module will challenge all students as is test of both calculating basic mathematics sums and also critiquing study design.
Information Technology Students will be taught and expected to use IBM SPSS statistics software package to analyse data and run hypothesis tests. They will be expected to process output from this software into a professional format.
Personal Development and Career planning Students will be encouraged to reflect on their perceptions and attitudes towards statistics as a subject prior to and after delivery of the module. They will also be prompted to reflect on how their understanding of statistics and relevant mathematical skills is developed through the module. Students will be required to apply the theory and skills learned to real-life data and assess the implications of what the various hypothesis tests are indicating. They will also be required to critique real-world data collection scenarios and have an appreciation of the potential biases involved.
Problem solving One explicit aim of the module is to develop the ability of students to undertake independent research projects, and a large element of the students’ learning will be directed to this end. Students will also be required to submit independent work which is linked to their own particular research topic, and to their ability to collect and analyse data.
Research skills Students will learn how to display and analyze data. They will also learn how to critique research articles and statistical study designs via class discussion and online tests.
Subject Specific Skills The subject specific skills here include the ability to correctly input data into software packages(s), run the necessary steps for the appropriate hypothesis test and interpret the results.
Team work Students will gain an appreciation of how large-scale statistics students involved many professionals with varied expertise working together to produce datasets, testable hypothesis and conclusions.


This module is at CQFW Level 7