Machine Learning Detects Pairwise Associations between SOI and BIS/BAS Subscales, making Correlation Analyses Obsolete

Abstract

We use AI techniques to statistically rigorously analyze combinations of query responses of two personality-related questionnaires. One questionnaire probes aspects of a participant's character (SOI) and the other avoidance of aversive outcomes together with approaches to goal orientated outcomes (BIS/BAS).

We use one-hot encoding, dimension reduction with a neural network (a seven-layer auto-encoder) and two clustering algorithms to detect associations between the twelve combinations of SOI and BIS/BAS groups. We discover that for most combinations more than one association exists.

Traditional, fallacious statistical methods cannot find these outcomes.

Keywords

• One-hot Encoding
• Autoencoder
• Neural Networks
• DBSCAN Clustering
• Spectral Clustering
• BIS/BAS
• SOI
• Heat Maps