Postgraduate Programme and Module Handbook 2023-2024 (archived)
Module BUSI4T215: Advanced Quantitative Data Analysis
Department: Management and Marketing
BUSI4T215: Advanced Quantitative Data Analysis
| Type | Tied | Level | 4 | Credits | 15 | Availability | Not available in 2023/24 | Module Cap | None. |
|---|
Prerequisites
- None
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To facilitate students’ in-depth engagement with a range of advanced approaches to quantitative data analysis.
- To develop students’ critical understanding of the logic of hypothesis testing and making causal claims.
- To provide students with hands-on experience in advanced analysis with state-of-the-science software tools.
- To facilitate students’ doctoral-level interpretation and writing skills for advanced quantitative data analysis.
- To develop students’ critical understanding of ethical implications when conducting quantitative research.
Content
- Hypothesis testing and causal inference in the context of advanced quantitative techniques.
- Quantitative data management and data quality.
- Multiple regression for testing simple and complex moderation and mediation models
- Advanced approaches to data analysis (confirmatory factor analysis, structural equation modelling, multilevel modelling, time series)
- Software tools for advanced data analysis (e.g., MPlus, -Stata).
Learning Outcomes
Subject-specific Knowledge:
- Critical understanding of statistical principles of data analysis
- Critical understanding of advanced quantitative data analysis approaches
- How to ensure data quality.
Subject-specific Skills:
- Ability to select relevant data analytical approaches
- Ability to interpret the results of advanced data analysis
- Ability to communicate quantitative research results, verbally and in writing.
Key Skills:
- Conducting advanced data analysis
- Using state-of-the-science software packages
- Interpreting advanced research results
- Writing and communicating advanced research results.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- The module will be delivered in a blended format, including lecture-type delivery, combined with tutor supported lab work (e.g., data analysis).
- The summative assessment (group component) and formative assessment are designed for students to learn from each other, strengthen the building of a doctoral community, and develop their teamwork skills for collaborative research.
- The summative assessment (individual) of the module is designed to facilitate students’ advanced quantitative data analysis and interpretation skills.
- Comprehensive reading and self-study materials will be provided online.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Workshops (online and classroom) | 10 | Weekly | 2 hours | 20 | |
| Preparation and Reading | 130 | ||||
| Total | 150 |
Summative Assessment
| Component: Written Assignment | Component Weighting: 50% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Group report | 2000 words | 100% | Same |
| Component: Written Assignment | Component Weighting: 50% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Individual Assignment | 2000 words | 100% | Same |
Formative Assessment:
Presentation of small group work related to topics covered in the module.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University