Module Information
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Presentation slides 5 Slides | 25% |
| Semester Assessment | Quantitative Report 2000 Words | 50% |
| Semester Assessment | Online Quizes 6 quizes | 25% |
| Supplementary Assessment | Presentation slides 5 Slides | 25% |
| Supplementary Assessment | Quantitative Report 2000 Words | 50% |
| Supplementary Assessment | Online Quizes 6 quizes | 25% |
Learning Outcomes
On successful completion of this module students should be able to:
Evaluate and critique quantitative research study design.
Summarise data using descriptive statistics or frequency tables appropriate to the data type.
Visualise data using high quality plots appropriate to the data type at a standard suitable for publication.
Select and apply appropriate statistical hypothesis tests to analyse a range of different quantitative datasets using statistical software.
Produce a professionally formatted report presenting the analysis and interpretation of statistical hypothesis tests addressing a real-world research question.
Brief description
This module is an integral component of the Research Training courses and is designed to meet the joint Research Councils statement on research training. Through this module students will gain a broad range of transferable skills which they can apply in a variety of research contexts. The taught sessions are a hybrid of theory covered in a lecture style, class discussion, and computer practicals. These sessions will cover basic statistical theory and methods, the assumptions underpinning different hypothesis tests, and the practical application of software. Additionally, students will be exposed to peer-reviewed research papers to familiarise themselves with the standard of report writing expected for successful completion of graduate level study. Students will be supported by online material including videos, lecture recordings, and a bank of examples relevant to a range of disciplines. They are expected to study using this material to then complete relevant assignments.
Aims
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.
Content
o Sample collection methods
o Generation of suitable hypothesis statements
o Statistical distributions including the normal distribution
o Use of statistical software: Data entry, summary statistics, splitting of data, plotting of data.
o Use of statistical software to run the following hypothesis tests:
• z-test
• Student’s t-tests
• Pearson correlation
• Linear and multiple regression
• Spearman correlation
• Chi squared test of goodness of fit
• Chi squares test of association
• ANOVA
• MANOVA
• Non-parametric equivalents: Friedman, Kruskal–Wallis, Mann – Whitney, and Wilcoxon Signed-rank tests
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | |
| Communication | Students will be expected to present their ideas in a short, narrated presentation. Following this they will produce a professionally formatted report presenting their analysis of a quantitative dataset. 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 | This module includes independent learning activities, including problem solving and online quizzes, to improve students own learning and performance. |
| Information Technology | Students will be taught and expected to use statistical software to analyse data and run hypothesis tests. They will be expected to process output from this software into a professional, publication -ready 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 has 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 re |
| 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 be required to submit independent work to demonstrate their ability to collect and analyse data. |
| Research skills | Students will learn how to summarise, visualise and analyse 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, run the necessary steps for the appropriate hypothesis test and interpret the results. |
| Team work | Through group problem solving tasks, students will gain an appreciation of how large-scale quantitative research projects work in practise. Students will work together to apply statistical hypothesis tests to quantitative datasets. |
Notes
This module is at CQFW Level 7