Module SSI2007 for 2021/2
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
SSI2007: Data Analysis in Social Science 3
This module descriptor refers to the 2021/2 academic year.
Module Aims
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.
On successfully completing the programme you will be able to: | |
---|---|
Module-Specific Skills | 1. clean and prepare your data for statistical analysis in R; 2. conduct statistical analysis using selected methods at the intermediate level in R; |
Discipline-Specific Skills | 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; |
Personal and Key Skills | 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 (such as R) for the analysis of social data at the intermediate level. |
Module Content
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
Data import with readr and data.table
Data manipulation with dplyr
Data visualisation with ggplot2
Iteration
Functions
Reproducible research and effective presentation of statistical results
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
---|---|---|
22 | 128 | 0 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
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 statistical report |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
---|---|---|---|
Github assignments | 2 Github assignments | 1-6 | Written feedback via Github |
Mock ELE test | 5 questions on ELE (about 30 minutes) | 1-6 | ELE feedback |
Summative Assessment
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
Coursework | Written exams | Practical exams |
---|---|---|
100 | 0 | 0 |
...and this table provides further details on the summative assessments for this module.
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
---|---|---|---|---|
ELE test at the end of the module | 50 | ELE test (90 minutes) | 1-6 | ELE feedback |
Final statistical report | 50 | 2000 words | 1-6 | Written feedback |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
---|---|---|---|
Final statistical report | Statistical report (2000 words) | 1-6 | August/September reassessment period |
ELE test | ELE test (90 minutes) | 1-6 | August/September reassessment period |
Indicative Reading List
This reading list is indicative - i.e. it provides an idea of texts that may be useful to you on this module, but it is not considered to be a confirmed or compulsory reading list for this module.
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/