The module will provide an introduction to the use of Statistical Analysis within the Research Process. It will begin by introducing and discussing different types of measurement and the practical problems of data entry in SPSSW. After discussing basic data description and transformation the focus will shift to Exploratory Data Analysis and the need to examine the data carefully. Simple approaches to summarising data and distributions will then be examined. This will then be followed by methods to test research hypotheses through bi-variate and multivariate methods that are used extensively in the Social Sciences. The final part of the module will look at various issues surrounding the practical issues of quantitative data analysis, such as how to find appropriate data and about presenting research outcomes.
Total contact hours: 33
Private study hours: 167
Total study hours: 200
Method of assessment
Main assessment methods
Coursework 3,000 word essay/research report – 60%
Coursework - In class test – three of – 30% (10% per test)
Coursework - In class 10 minute seminar presentations – 10%
Reassessment instrument - 100% coursework.
Field, A.P. (2013) Discovering statistics using IBM SPSS: (and sex and drugs and rock 'n' roll). Fourth Edition. UK: Sage.
Pallant, J. (2010) SPSS Survival Manual, 4th edition, McGaw-Hill.
Bryman and Cramer (2011) Quantitative Data Analysis with SPSS: A Guide for Social Scientists, Taylor & Francis.
See the library reading list for this module (Canterbury)
The intended subject specific learning outcomes are as follows. On successfully completing the module students will be able to:
1.Demonstrate a critical understanding of the theoretical and methodological basis of quantitative research, as well as some of the limitations it may have. This includes the ability to evaluate the strength and the weaknesses of the analysis methods as well as knowing how and when to use or combine quantitative research
2.Demonstrate a critical understanding of the statistical techniques applied in social science research. More specifically, students are expected to be able to manage data using SPSS and run analysis using basic methods of descriptive and inferential statistics as required by the ESRC Guidelines to critically support one's own research. This would include comparative as well as longitudinal methods.
3.Demonstrate the ability to present one's own quantitative analysis outcome both verbally and in written work. In addition, have the ability to critically evaluate the statistical methods used in the research literature as well as policy documents.
4.Demonstrate a critical understanding of how to find and evaluate existing secondary data sets. This includes accessing data from the UK Data Archive, as well as other comparative data from other sources. In addition, students are expected to know how to choose a valid sample from the existing data to fit their own research interest
The intended generic learning outcomes are as follows. On successfully completing the module students will be able to:
1.Consolidate their skills in presentation and debate, both written and verbal, to a level commensurate with postgraduate study
2.Identify and solve problems that are common in social research
3.Consolidate their skills in collating complex material using databases and the internet as appropriate
4.Manage their time, prioritise workloads and manage stress as well taking responsibility for their learning and professional development
The intended subject specific learning outcomes and, as appropriate, their relationship to programme learning outcomes
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Credit level 7. Undergraduate or postgraduate masters level module.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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