Module SOC2094 for 2018/9
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Undergraduate Module Descriptor
SOC2094: Data Analysis in Social Science III
This module descriptor refers to the 2018/9 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 | Reading, preparation and writing of the statistical report |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
Web based and electronic resources:
“Data Analysis in Social Science 3”: http://abessudnov.net/dataanalysis3/