Quantitative methods

Aims

Aims

This module aims to give you a firm grounding in the theory and practice of quantitative data analysis, which you will be able to use in your own research.

You will learn about a wide range of statistical techniques, which are essential for doing data-driven research and critically evaluating arguments based on quantitative data. You will also receive practice in applying these techniques to actual data using statistical software.

Programme

• Programme

Contact hours

The module lasts one term and is taught for three hours a week (1 hour lecture and 2 hour practical).

Teaching programme

The module is designed to give you a background in quantitative data analysis. We will cover the following topics:

• motivations for quantitative data analysis
• descriptive statistics and data visualisation
• basic probability theory and the foundations of statistical hypothesis testing
• methods for statistically assessing the strength and significance of relationships and differences within quantitative data sets
• multiple regression

You will receive substantial training in the use of a statistical software package called R, which will allow you to apply the techniques we learn about to a range of different data sets.

Teaching materials

Required reading:

• Langdridge, D. (2004). Research methods and data analysis in Psychology. Harlow, England: Pearson Education Limited.
• Baayen, R. H. (2006). Analyzing linguistic data: A practical introduction to statistics using R. Cambridge: Cambridge University Press

The text will be supplemented by relevant reading which will be made available to students.

Assessment

Assessment

Formative work

• data analysis exercise, involving data visualisation methods and descriptive statistics

Assessment

• a two-part closed exam:

part 1 (theory): a set of questions focusing on your understanding of descriptive and inferential statistics
part 2 (practice): you'll have to perform a few simple tasks on a computer using R, a statistical software package