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Track-specific requirements

Psychology: Behavioural Data Science

1. Research methodology

Students should have a good understanding of the following topics: observational versus experimental studies, (quasi-)experimental designs, randomization, sampling, confounding, bias, test construction, measurement error, test reliability, and test validity.

Student should be able to explain what type of conclusions can be drawn from different types of study designs (and what not), and should be able to analyse the quality (i.e., reliability and validity) of a psychological measurement instrument.

2. Statistics

2.1 Basic statistics

Students should have a good understanding of the following topics: descriptive statistics, graphical summaries of data, (conditional) probabilities, random variables, populations versus samples, sampling distributions, inferential statistics, confidence intervals, and hypothesis testing.

Students should be able to summarise univariate and bivariate data distributions, and should be able to test null hypotheses by using the General Linear Model (t-tests, ANOVA, regression).

2.2 Advanced statistics

Students should also have a basic understanding of some multivariate statistical models and latent variable models.

Students should be able to run and interpret the outcomes of a multivariate analysis (e.g., repeated measures analyses, MANOVA, principal component analysis) and a latent variable model (e.g., factor analysis, item response theory, structural equation modelling).

3. Programming (in R)

Students should have a good understanding of the basics of programming in a high level languages, preferably R, in such a way that they can can read data, can analyse data, (both numerically and graphically), including fitting general linear models, and that they can write some basic functions themselves.