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Module description:

Module Content

This course is designed to provide you with proficiency in using and interpreting quantitative data and statistics and will expect no prior knowledge of statistical analysis. You will be taught through a series of lectures and practical sessions, working with relevant data sets in order to get a feel for the manipulation of real data. You will have the opportunity to consider application of statistical analyses to your own research plans and to gain familiarity with the open source computing package R.

Indicative Topics

Topics may include descriptive statistics, hypothesis testing and probability distributions, non-parametric methods, univariate tests of group difference, correlation and regression analysis, and the relationship between quantitative and qualitative methods.

Learning Outcomes

Having completed the course you will:

• Be able to demonstrate critical awareness of basic data types and statistical approaches used in contemporary research

• Have an understanding of the mathematical foundations and limitations of basic statistical techniques

• Be able to demonstrate the correct selection, use, and interpretation of basic statistical techniques on a range of data sources and types

• Feel confident in identifying opportunities and limitations of mixed-methods approaches involving statistical techniques

Teaching Delivery

Sessions consist of a lecture followed by supervised and unsupervised practical exercises. The sessions integrate the use of computers throughout.