Experimental Methods in Business Research

Information

The course is built on three pillars: Testing research ideas with clean and rigorous experimental designs, applying the appropriate statistical techniques necessary to analyse these designs, and properly interpreting the implications of the observed results in light of the study design and research question. Topics include: causal inference, experimental designs (factorial designs, within- vs. between-subject designs, mixed designs), validity of designs (internal, external, criterion-related, and construct validities), experimental caveats (demand characteristics, confounds, experimenter expectancy, checks, measurement order, control variables), applying statistics to experimental designs (e.g., simple effects, simple slopes, contrasts, outliers, statistical power, simple mediation, moderated mediation, mediated moderation), data visualization, and scientific integrity (researcher degrees of freedom, questionable research practices, transparency, p-hacking) 

We will examine these topics from the perspective of an applied behavioural researcher (with examples from fields such as marketing, organizational behaviour, economics, and psychology). Our focus is thus very practical—to help you overcome any issues that might arise in designing and conducting your experiment, analysing your data, writing up your results, and getting your paper published. An intermediate proficiency in a programming language (preferably R) is strongly encouraged for this class.

As in previous years, we may be able to collect data in the Erasmus Behavioural Lab.