"Classifying D/s Profiles Without Prior Assumptions: An Application of Cluster Analysis to Social Data"

Area of Science:

• Psychology
• Sociology
• Human Sexuality

Background:

• Dominant/submissive role-play (D/s) involves specialized roles like Mistress, Master, Slave, Switch, Sadist, and Masochist.
• Existing typologies often rely on binary oppositions or single spectra, which may oversimplify individual preferences.

Purpose of the Study:

• To empirically investigate and establish a workable typology of individuals based on their D/s role affinities.
• To test the hypothesis that a two-dimensional model best represents D/s profiles.

Main Methods:

• Cluster analysis was applied to a large dataset of individualized results (profiles) from the BDSM Test, an anonymous web survey.
• A replication technique was utilized to establish the optimality of the chosen clustering scheme and the number of clusters.
• Analysis included a substantial sample size (n = 236,353) of self-reported D/s role affinities.

Main Results:

• Empirical evidence suggests that a simple binary opposition or single spectrum is insufficient for classifying individuals by D/s role affinities.
• Cluster analyses indicate that a two-dimensional typology provides a more accurate representation of the complex patterns observed in D/s profiles.
• The study validates a specific clustering scheme and number of clusters through a detailed replication technique.

Conclusions:

• The findings challenge simplistic models of D/s role preferences, supporting a more nuanced, multi-dimensional understanding.
• A two-dimensional typology offers a more empirically sound framework for characterizing individuals within the spectrum of D/s dynamics.
• This research provides a robust, data-driven classification of D/s role affinities based on a large-scale survey analysis.