Module Details

SC504-7-AU-CO: Introduction To Quantitative Analysis

Year: 2017/18
Department: Sociology
Essex credit: 20
ECTS credit: 10
Available to Study Abroad / Exchange Students: Yes
Full Year Module Available to Study Abroad / Exchange Students for a Single Term: No
Outside Option: No

Supervisor: Professor Nick Allum
Teaching Staff: Dr Nick Allum
Contact details: Michele Hall, Graduate Administrator, Telephone 01206 873051, Email:

Module is taught during the following terms
Autumn Spring Summer

Module Description

TThis module is a practical introduction to analysing quantitative data. Using a combination of lecture and computer lab based formats, the module is intended to provide participants with an understanding of the principles of quantitative data analysis and their practical application. The primary focus is on the application of statistical techniques for analysing survey data, although the methods covered are applicable to many other forms of quantitative data. As well as enabling participants to conduct investigations relevant to their own research, it will also equip them to be a critical user of other research.

Topics covered include descriptive statistics, sampling, variance estimation and statistical inference, hypothesis testing, bivariate association, linear and logistic regression model. The module will be practically oriented, focusing on the application of techniques and the interpretation of results. The software used in this module will be STATA. Expertise in using this software will be built up over the module and prior knowledge is not assumed.

At the end of the module, participants should be able to: identify and apply appropriate statistical techniques for a variety of research questions; present and interpret statistical findings; critically evaluate research that uses statistical methods.

The module is assessed with two data analysis reports.

Learning and Teaching Methods


100 per cent Coursework Mark


Assessment The module is assessed with two written assignments, the first is a short exercise that contributes 30% towards overall module mark; the second is a more substantial data analysis exercise (70% towards overal)

Other information

Please be aware that this module includes a lot of incremental work and you will need to attend each week. You will also need to use computer software only available in the campus IT labs in order to complete assessments. The module does not lend itself to home study.


  • Indicative Reading
  • Agresti, A. and Finlay, B. (2009) Statistical Methods for the Social Sciences, 4th Edition, Prentice Hall.
  • Acock, A. (2008) A Gentle Introduction to Stata (2nd Ed.). Stata Press.
  • Allison, P.D. (1999), Multiple regression: a primer, Pine Forge Press
  • Freedman, D., Pisani, R. and Purves, R. (2007) Statistics (4th Ed). Norton. London.
  • Long, J. Scott, (1997), Regression Models for Categorical and Limited Dependent Variables. Sage Publications.
  • Rosenberg, M (1968), The logic of survey analysis, Basic Books
  • Treiman, D. (2009). Quantitative data analysis: doing social research to test ideas. Jossey-Bass.

Further information