Treffer: A Tutorial for Exploratory Research: An Eight-Step Approach

Considerable effort in psychological science is currently directed towards confirmatory practices. Much less attention has been devoted to how to do exploratory research. In this article, the authors support researchers in expanding their methodological toolbox by adding one more technique of exploratory research. Most of this article is a hands-on tutorial that explains how exploration can be done using supervised machine learning techniques and how to choose them, ultimately leading to a demonstration of the best performing technique, conditional random forests. The practical part of this tutorial explores one of the authors’ own datasets, the Human Penguin Project (IJzerman, Lindenberg et al., 2018). The reader can follow the tutorial by recreating the authors’ analyses in her own RStudio, apply annotated code to her own data or other secondary data, and repeat the steps. The authors show how to get familiar with datasets the researcher wants to use for machine learning, inspect it in useful ways, and make predictions using machine learning algorithms. We close with describing the limitations related to causal inference and clarifying that finding robust patterns does not equate generating a comprehensive theory. This tutorial requires basic knowledge of statistics and programming language R (R Core Team, 2016), but the authors provide resources for absolute beginners in the Supplemental Materials.