Undergraduate Module Descriptor

SOC3094: Data Analysis in Social Science III

This module descriptor refers to the 2016/7 academic year.

Module Aims

The aim of this module is to introduce you to more advanced quantitative techniques for the analysis of social data that goes beyond simple descriptive statistics. More specifically, we will study different data reduction and classification techniques, such as factor analysis and cluster analysis. After completing this module you will be able to understand and interpret the results of multivariate statistical analysis frequently reported in social science journals and policy papers, as well as independently conduct analysis using R, a statistically oriented programming language. Multivariate statistical analysis is applied for data analysis outside the academia, and employers often value these skills.

Intended Learning Outcomes (ILOs)
This module's assessment will evaluate your achievement of the ILOs listed here – you will see reference to these ILO numbers in the details of the assessment for this module.
On successfully completing the programme you will be able to:
Module-Specific Skills	1. carefully interpret and explain in detail the results of multivariate statistical analysis reported in the social science literature; 2. conduct multivariate statistical analysis using selected methods independently at the advanced level using statistically oriented programming language, R;
Discipline-Specific Skills	3. promptly recognize research designs and data where various techniques of multivariate statistical analysis can be applied; 4. clearly explain the results of multivariate 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 6. use general-purpose statistical software (such as R) for the analysis of social data at the advanced 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:

- Factor analysis

- Cluster analysis

- Correspondence analysis

- Multidimensional scaling

- Latent class analysis

- Data manipulation and visualization in R

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
22.5	127.5	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	6	6 x 1 hour lectures
Scheduled learning and teaching activity	16.5	11 x 1.5 hour computer lab sessions
Guided independent study	77.5	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).

http://vle.exeter.ac.uk/