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Description
This module provides a practical introduction to research methods and statistics for experiments in the cognitive and behavioural sciences. After a general introduction (What is data? Why do we need statistics? What is the difference between experiments and other research designs?) we will introduce the free statistical programming language R, which will be used throughout the module for all practical aspects of data analysis. The focus in this first parts of the module will be on a general introduction to statistical computing (e.g., files and folders), data handling (e.g., reading data and data preparation), and data visualisation. After this, we will introduce the most important statistical methods for analysing experimental data, analysis of variance (ANOVA) and analysis of covariance (ANCOVA) using different real data sets from the published literature. These are versatile methods that can be applied to simple designs (e.g., single factor with two groups), complex design with multiple factors, designs involving continuous predictors, as well as designs involving repeated measures (which are common in cognitive domains). In addition to the practical introduction to how to perform a statistical analysis for experimental data, the module will also provide a theoretical introduction to null hypothesis significance testing (NHST), the most popular statistical framework for inferential statistics. The theoretical part focuses on the logic of the inferential machinery using data simulation as well as common pitfalls and problems associated with a mindless application of NHST.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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