Research Methods I
| Module title | Research Methods I |
|---|---|
| Module code | BEEM136 |
| Academic year | 2022/3 |
| Credits | 15 |
| Module staff | Dr Rish Singhania () |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 12 |
|---|
Module description
This module provides an introduction to the techniques involved in data handling and analysis.
Module aims - intentions of the module
This module aims to provide a thorough introduction to the techniques involved in effective data handling, analysis and visualization required to undertake PhD level quantitative research.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. use statistical tools and software packages
- 2. repeat steps required to work with large datasets
- 3. transform raw data into meaningful insight
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. read and work with current economic data.
- 5. critically analyse and visualize economic data
- 6. list data sources commonly used to analyse economic models
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. apply quantitative skills and handle logical and structured problem analysis.
- 8. apply inductive and deductive reasoning involving data
- 9. apply essential research skills.
Syllabus plan
- Fundamentals of programming
- Handling and manipulating vectors and matrices
- If-then conditional statements
- For loops and other iterative operations
- User defined functions and the ability to use built-in functions
- Handling and manipulating strings
- Tools to deal with databases and tables
- Data visualization
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 32 | 118 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching activities | 22 | Lectures |
| Scheduled learning and teaching activities | 10 | Tutorials |
| Guided independent study | 118 | Reading, preparation for classes and assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Practice Problems | Varies | 1-9 | Oral/Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 20 | 80 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Empirical Project | 80 | 2500 words (10-12 sides of A4) | 1-9 | Oral/Written |
| Average of bi-weekly problem sets | 20 | Bi-weekly problem sets with at most 3 questions each | 1-9 | Oral/Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Empirical Project (80%) | Empirical Project 80% | 1-9 | August examination period |
| Average of bi-weekly problem sets (20%) | Single problem set (20%) | 1-9 | August examination period |
Re-assessment notes
*Deferral of an individual online test may result in an average being taken of tests that have been taken
Indicative learning resources - Basic reading
- R Programming for Data Science, Peng RD (2020)
- Introduction to Data Exploration and Analysis with R, Mahoney (2019)
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | Only available to MRes Economics PhD pathway |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 24/06/2019 |
| Last revision date | 30/08/2022 |
