Accessibility statement

Introduction to Quantitative Methods & Data Analysis - RSS00006M

« Back to module search

  • Department: Research Centre for Social Sciences
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2022-23

Module summary

The module introduces graduate students to a range of useful techniques of data analysis. It is a mix of lectures and practical exercises using statistical data analysis software.

Module will run

Occurrence Teaching period
A Autumn Term 2022-23

Module aims

The module aims to introduce graduate students in the social sciences to a range of common quantitative data analysis skills and techniques but to understand the key concepts of quantitative analysis. At the end of the module students should be able to both carry out their own analyses of large scale statistical datasets using statistical data analysis software and be able to critically interpret the use of such techniques in the work of others. Students will be taught how to manipulate, explore and analyse large datasets, including secondary data. They should be able to understand the rationale for using quantitative data when needed but also particular techniques over others. At the end of the module, students will be expected to be able to formulate a research problem and question and related hypotheses and to use an appropriate quantitative technique to test these hypotheses. Students should be able to provide an adequate justification for the choice of the technique used. A critical attitude with regard to the various quantitative methods is then encouraged. To help students in this approach, many examples of the way statistical techniques have been used in social sciences will be given and discussed.

 

Module learning outcomes

On completing this module students will be:

  • able to understand how specific research questions can be answered by quantitative methods of analysis and what kind of research designs quantitative analysis requires
  • competent in understanding a broad range of descriptive and inferential statistical procedures both exploratory and confirmatory (e.g. contingency table, chi-square test, ANOVA, linear and logistic regression)
  • able to manage, manipulate, explore and analyse large quantitative datasets, including secondary data
  • able to undertake bivariate and multivariate analysis
  • competent in using statistical data analysis software
  • capable of understanding the concept of hypothesis testing and statistical significance and notions of probability and sampling
  • able to understand notions of generalisability, causality, validity and reliability
  • able to develop a critical understanding of the methods studied

Indicative assessment

Task % of module mark
Essay/coursework 40
Essay/coursework 60

Special assessment rules

None

Indicative reassessment

Task % of module mark
Essay/coursework 40
Essay/coursework 60

Module feedback

All feedback on summative work will be timely and will provided in accordance with University policy. Feedback on Report 1 will be provided in time to support work on Report 2.

 

Indicative reading

  • Fielding, J. and Gilbert, N. (2006) Understanding Social Statistics. London: Sage, 2nd ed.
  • Treiman, D. J. (2014). Quantitative data analysis: Doing social research to test ideas. John Wiley & Sons.  
  • Wetcher-Hendricks, D. (2011). Analyzing quantitative data: An introduction for social researchers. John Wiley & Sons.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.