This intensive five-day course offers an introduction to the basics of applied statistics for data analysis in the social sciences and humanities. It is aimed at social science or arts and humanities PhD students who have little or no training in quantitative methods. The aim of the course is to provide students with a sound understanding of: the nature of quantitative data; how to describe this data; how to use basic statistical methods to make inferences about associations between variables of interest; and how to practically apply these methods using the statistical software package SPSS.

The topics covered in the course will include:
• The nature of data: Spreadsheets and what they contain.
• Exploring and describing data
• The basics of inference: Populations, samples and sampling distributions
• Inference for population means and population proportions: confidence intervals, t-tests and z-tests
• Testing for differences in population means across groups
• Contingency tables and tests for association between two categorical variables
• Association between two quantitative variables: correlation and simple linear regression
• Statistical control and multiple linear regression

This is an introductory course. If you have already taken a course on introductory statistics, and/or if you are comfortable with the topics listed above, this course will not be very useful for you. Given, that there will be limited places on this course, please do not sign up for it if you already have a good understanding of the material covered. Also, please do not sign up for this course if you cannot attend all of the scheduled sessions.

The course does not assume prior knowledge of statistics, nor any experience of working with SPSS. Because the aim of the course is to develop an applied understanding of statistical methods, we will not rely heavily on maths when covering these methods (e.g. we will not go through the formal proofs). However, we will sometimes use simple mathematical formulae to convey the logic of a statistical method.  In these instances, these formulae will be explained as fully as possible.