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 Skills1. 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 Skills3. 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 Skills5. 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.

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.

Basic reading:

 D.J.Bartholomew et al., Analysis of Multivariate Social Science Data, 2nd ed, Chapman and Hall/CRC (2008).

 Supplementary reading:

I.Borg & P.Groenen, Modern Multidimensional Scaling: Theory and Applications, 2nd ed, Springer (2005).

B.Everitt & T.Hothorn, An Introduction to Applied Multivariate Analysis with R, Springer (2011)

M.Greenacre, Correspondence Analysis in Practice, 2nd ed., Chapmand and Hall/CRC (2011).

S.A.Mulaik, Foundations of Factor Analysis,  2nd ed. Chapman and Hall/CRC (2010).

P.Spector, Data Manipulation with R, Springer (2008).

A.Unwin, Graphical Data Analysis with R, CRC Press (2015).

N.Matloff, The Art of R Programming, No Starch Press (2011).

W.Chang, R Graphics Cookbook, O’Reilly (2013).