Module Information
Module Identifier
GS23810
Module Title
Quantitative Data Analysis
Academic Year
2023/2024
Co-ordinator
Semester
Semester 1
Reading List
Course Delivery
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Coursework workbook (7 x A4 pages) | 100% |
| Supplementary Assessment | Resubmission of coursework workbook | 100% |
Learning Outcomes
On successful completion of this module students should be able to:
1. Appreciate the role and types of quantitative data presentation methods and analyses.
2. Demonstrate skills in the probability-based evaluation of population difference through computer-based statistical sample comparison.
3. Develop an understanding of the nature of bivariate relationships through familiarity with the principles and practice, through computer-based practical application, of regression and correlation analysis.
4. Demonstrate proficiency in the computer-based evaluation of (i) non-linear bivariate relationships and (ii) multivariate relationships.
Brief description
This module is to provide all DGES Level 2 single-honours students with a firm grasp of computer-based methods of quantitative data presentation and analysis.
The module will be presented through lectures and practical sessions over weeks 1 to 8 of semester 1. These sessions will normally comprise a 1-hour lecture (Weeks 1 – 6) followed by a 2-hour computer-based practical session (Weeks 1 – 8, giving students two clear weeks - supported by continuing practical sessions - following the last lecture to complete Workbooks). Each practical session will include staff and demonstrator support, thereby addressing each assessed component within one week of its parent lecture.
The module will be assessed through a Workbook of seven exercises submitted at the end of Week 8. This timing will ensure students have completed the module coursework and submissions before focusing on end-of-semester submissions for other modules. It will also allow time to provide students with marks and feedback before the end of the semester.
The course is structured so that each student’s Workbook will be completed progressively through the course, with updates to each exercise checked at the following two practical sessions.
The module will be presented through lectures and practical sessions over weeks 1 to 8 of semester 1. These sessions will normally comprise a 1-hour lecture (Weeks 1 – 6) followed by a 2-hour computer-based practical session (Weeks 1 – 8, giving students two clear weeks - supported by continuing practical sessions - following the last lecture to complete Workbooks). Each practical session will include staff and demonstrator support, thereby addressing each assessed component within one week of its parent lecture.
The module will be assessed through a Workbook of seven exercises submitted at the end of Week 8. This timing will ensure students have completed the module coursework and submissions before focusing on end-of-semester submissions for other modules. It will also allow time to provide students with marks and feedback before the end of the semester.
The course is structured so that each student’s Workbook will be completed progressively through the course, with updates to each exercise checked at the following two practical sessions.
Content
The module will cover the following topics, broadly equating to lectures 1 - 6:
1. Principles of quantitative analysis, introduction to data types, descriptive statistics and data presentation.
2. Evaluating the statistical difference between two populations through sample analysis using SPSS (‘Mann-Whitney U test’ and ‘Student’s t test’)
3. Evaluating the statistical difference between more than two populations through sample analysis using SPSS (‘Kruskall-Wallace H test’ and ‘Analysis of Variance (ANOVA)’
4. Evaluating bivariate relationships through linear regression and correlation using SPSS
5. Evaluating bivariate relationships through non-linear and multiple regression and correlation using SPSS
6. Overview and open question and answer session
1. Principles of quantitative analysis, introduction to data types, descriptive statistics and data presentation.
2. Evaluating the statistical difference between two populations through sample analysis using SPSS (‘Mann-Whitney U test’ and ‘Student’s t test’)
3. Evaluating the statistical difference between more than two populations through sample analysis using SPSS (‘Kruskall-Wallace H test’ and ‘Analysis of Variance (ANOVA)’
4. Evaluating bivariate relationships through linear regression and correlation using SPSS
5. Evaluating bivariate relationships through non-linear and multiple regression and correlation using SPSS
6. Overview and open question and answer session
Module Skills
| Skills Type | Skills details |
|---|---|
| Application of Number | Use of mathematics and statistics in computer-based exercises. |
| Communication | Clear and effective data presentation through e.g., bivariate plots. |
| Improving own Learning and Performance | Meeting coursework deadlines in preparing and submitting two Workbooks. |
| Information Technology | Computer-based quantitative data manipulation and statistical analysis. |
| Personal Development and Career planning | Develop numeracy and proficiency in MS Excel and SPSS. |
| Problem solving | Analyzing data to address research problems. |
| Research skills | N/A |
| Subject Specific Skills | Develop analytical skills in preparation for Level 3 dissertation/project. |
| Team work | Students are encouraged to help each other in developing proficiency in the computer software packages (but submitted Workbooks are individual). |
Notes
This module is at CQFW Level 5