Data Analysis in Social Science 3
| Module title | Data Analysis in Social Science 3 |
|---|---|
| Module code | SSI3003 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Andrei Zhirnov () |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 60 |
|---|
Module description
Basic knowledge of statistics and data analysis is often not enough for dealing with more complicated problems in the social sciences, as well as in market research, applied policy analysis, and data-driven journalism. This module introduces you to more advanced techniques for social data analysis using the statistical programming language R and the tidyverse framework (or alternatively Python and pandas). These techniques are especially useful while working with large and “messy” data sets. While some statistical theory is covered in this module, the discussion of statistical concepts is generally non-mathematical and intuitive and is based on numerous examples from social sciences. The module assumes familiarity with basic descriptive statistics and linear regression analysis.
Module aims - intentions of the module
The aim of this module is to introduce you to more advanced quantitative techniques for the analysis of social data. More
specifically, you will learn how to clean, transform, reshape and visualise data in R, a statistical programming language, and
tidyverse, a collection of tidyverse packages. You will also learn the fundamentals of programming in R, such as conditional
statements, loops and functions. After completing this module, you will be able to independently conduct data analysis in R.
Employers in many industries value this skill.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. clean and prepare your data for statistical analysis in R or Python;
- 2. conduct statistical analysis using selected methods at the advanced level in R or Python;
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. apply statistical data analysis techniques to social science problems;
- 4. clearly explain the results of statistical analysis in substantive terms and relate them to substantive social science problems;
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. report the results of statistical analysis in writing in a way that would be understood by non-specialists; and
- 6. use general-purpose statistical software for the analysis of social data at the advanced level
Syllabus plan
Whilst the module’s precise content will vary from year to year, it is envisaged that the syllabus will cover some of the following themes:
- Data types and structures in R or Python
- Data import with readr and data.table / pandas
- Data manipulation with dplyr / pandas
- Data visualisation with ggplot2 / matplotlib
- Iteration
- Functions
- Reproducible research and effective presentation of statistical results
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 22 | 128 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 22 | 11 x 2 hour lectures / computer lab sessions |
| Guided independent study | 78 | Reading and preparation for lectures and lab sessions |
| Guided independent study | 50 | Data analysis and writing of the data report |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Github assignments | 2 Github assignments (5 problems each) | 1-6 | Written feedback via Github |
| Mock ELE test | 5 questions on ELE (about 30 minutes) | 1-6 | ELE feedback |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| ELE test | 50 | 1 hour | 1-6 | ELE feedback |
| Data report | 50 | 1,500 words | 1-6 | Written feedback |
| 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 |
|---|---|---|---|
| ELE test | ELE test (1 hour) | 1-6 | August/September reassessment period |
| Data report | Data report (1,500 words) | 1-6 | August/September reassessment period |
Indicative learning resources - Basic reading
- G.Grolemund, H.Wickham. R for Data Science. O’Reilly (2017). Available at https://r4ds.had.co.nz/
- W.Chang, R Graphics Cookbook, 2nd ed., O’Reilly (2019). Available at https://r-graphics.org/
Indicative learning resources - Web based and electronic resources
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | SSI1005 and SSI1006 |
| Module co-requisites | SSI2005 if not taken before |
| NQF level (module) | 6 |
| Available as distance learning? | No |
| Origin date | 30/03/2016 |
| Last revision date | 06/10/2022 |
