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Unit information: Further Quantitative Methods in 2016/17

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Further Quantitative Methods
Unit code SPOLM0016
Credit points 20
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Turner
Open unit status Open

Participants should usually have already taken the unit ‘Introduction to Quantitative Research Methods in the Social Sciences’ (SPOLM0015), or be able to demonstrate equivalent expertise.



School/department School for Policy Studies
Faculty Faculty of Social Sciences and Law

Description including Unit Aims

Analysing Quantitative Data

This unit builds upon the new DTC module, Introduction to Quantitative Research Methods in the Social Sciences (SPOLM0015), by focusing upon techniques for the analysis of quantitative data. The unit covers three main topics:

  • The practice of secondary data analysis using survey sources based upon a range of statistical methods and ‘hands-on’ exercises using SPSS
  • The circumstances in which particular techniques can be applied and the strengths and weaknesses of different methods in informing policy and practice
  • The interpretation of quantitative data and the dissemination of results in accessible ways which can inform policy and practice.

Intended Learning Outcomes

Upon completion of this unit student should be able to:

  • Use descriptive methods in order to explore the properties of quantitative data and select an appropriate strategy of analysis for data of different types
  • Analyse the relationship between variables using a range of parametric and non-parametric approaches
  • Compare groups of cases using both parametric and non-parametric tests
  • Demonstrate awareness of the strengths and limitations of statistical evidence in informing policy and practice
  • Apply principles for the effective dissemination of quantitative evidence to policy and practitioner audiences using appropriate data visualisation methods

Teaching Information

Lecture and demonstration. Many of the classes in this module involve an emphasis upon developing computer-based statistical analysis skills and will therefore be lab-based. These sessions will involve on-line classroom-based exercises designed to develop competency in using SPSS for the analysis of quantitative data.

Assessment Information

Formative assessment will be primarily by means of student presentations delivered as part of the teaching program. Students will be asked to work in small groups to develop a research design in order to investigate a key social policy problem (e.g. ill health, crime, poverty) based upon exploration of a selected UK Data Archive teaching data set. Students will be asked to present their proposed research and will have an opportunity to receive feedback on this during the session.

Formative self-assessment by means of multiple choice questionnaires is also available to participants via the package “Statistics for the Terrified”. This will enable participants to better evaluate their progress, and to identify areas for revision or further development.

Many of the unit sessions are based upon lab-based activities involving analysis of large-scale datasets using the SPSS package. Students are asked to complete these tasks and write up their results in the associated workbooks which we review with students at the end of each session.

Summative assessment will be by means of a written assignment of not more than 4,000 words. Participants will be asked to apply the knowledge and skills they have developed during the course of the unit to the investigation of a key social policy problem based upon the secondary analysis of a large scale teaching data set.

Reading and References

  • Bryman, A. and Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 and 16 : a guide for social scientists . London: Routledge.
  • Dorling, D. and Simpson, S. (Eds.) (1999). Statistics in Society: The arithmetic of politics. London: Arnold.
  • Levitas, R. & Guy, W. (1996). (Eds.) Interpreting Official Statistics. Routledge: London.
  • Field, A. (2013). Discovering statistics using IBM SPSS statistics : and sex and drugs and rock 'n' roll. (4th Ed.) London: Sage.
  • Marsh, C. and Jane Elliott, J. (2008). Exploring data: an introduction to data analysis for social scientists. . Cambridge, Polity Press.
  • Tufte, E. (1990). Envisioning Information. Connecticut, Graphics Press.