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 probes aspects of a participant’s tendency for uncommitted sexual behavior (SOI-R) 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, Auto-Encoder, Neural Networks, Dbscan Clustering, Bis/Bas, Soi, Kernel Density Estimation, Heat Maps

DOI: 10.54941/ahfe100903