Here’s a description of a new course I’m teaching next quarter (Spring, 2019).

PSYCH 548 ADV QUANT PSYCH: Exploratory Data Analysis in Psychology

From very early, Psychology as a discipline has emphasized hypothesis driven research. At the same time for decades, exploratory statistical approaches and algorithms, such as exploratory factor and principle components analysis have been key analytical approaches. Additionally, machine learning and other exploratory algorithms are being embraced across many scientific domains, including Psychology. What about this disconnect between a historical disdain for exploratory analysis with the current interest in complex exploratory computational procedures? How have and could we think about exploratory data analysis in Psychology? How should exploratory work be used to extend Psychological knowledge and theory?

In this seminar, I hope we will consider these issues, as well as some exemplar models and approaches. We’ll read historical and more recent papers on exploratory analysis generally, as well as focus on some specific models (some possibilities include exploratory factor analysis, canonical correlation, cluster analysis, some machine learning approaches, exploratory SEM; suggestions welcome). Classes will focus on discussion of the material, implications for research (broad, as well as specific to a single research area). You will be encouraged to work with your own data for the class, and we’ll strive to work some of those analyses into class meetings. I’m considering brief weekly reaction papers or a brief analyses using some focal model on your own data. A final paper will compare the scientific upshot of a few exploratory approaches applied to your data.

My goal is that we come out of this seminar thinking more broadly and critically about exploratory analysis in Psychology.