Experimental Methods in Business Research
The goal of this course is to learn how to design, analyse and report field or laboratory experiments.
The course is built on three pillars: Testing research ideas with clean and rigorous experimental designs, applying the statistical techniques necessary to analyse these designs, and writing a research report about it that conforms to journal standards. Topics include: causal inference, experimental designs (factorial designs, within- vs. between-subject designs, mixed designs), validity of designs (internal, external, statistical, construct), 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 (Python or R) is strongly encouraged for this class.
25% class participation, 75% assignments.
Further reading and resources will be posted on Canvas after each lecture.
As in previous years, we may be able to collect data in the Erasmus Behavioural Lab.