The module introduces graduate students to a range of useful techniques of data analysis. It is a mix of lectures and practical exercises using SPSS.
Module will run
Autumn Term 2017-18
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 SPSS 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 SPSS
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
% of module mark
Essay/coursework Report 1(1500 words)
Essay/coursework Report 2 (2000 words)
Special assessment rules
% of module mark
Essay/coursework Reports (3500 words)
Students will receive written feedback on report 1 at the end of week 10 of term 1.
Students will receive feedback on report 2 in week 5 of term 2.
Students will receive feedback from resubmission/reassessment three weeks after the deadline for re-submission.
Acton C. and Miller R. (with Maltby J. and Fullerton D.) (2009). SPSS for Social Scientists. Basingstoke: Palgrave MacMillan, 2nd ed.
Fielding, J. and Gilbert, N. (2006) Understanding Social Statistics. London: Sage, 2nd ed.
Field A. (2013) Discovering Statistics using SPSS for Windows. London: Sage, 4th ed.
Pallant, J. (2016) SPSS Survival Manual. New York: Open University Press, 6th ed.