Psychometrics of Drawmetrics: An Expressive-Semantic Framework for Personality Assessment

Area of Science:

• Psychology
• Personality Science
• Computational Social Science

Background:

• The Five-Factor Model (Big Five) is a dominant framework for personality traits.
• Drawmetrics (DM) is an expressive-semantic personality system.
• Linking different personality systems enhances theoretical integration and measurement.

Purpose of the Study:

• To investigate the relationship between Drawmetrics (DM) and the Five-Factor Model (Big Five).
• To determine if DM can be mapped onto the Big Five using computational methods.
• To assess the degree of overlap and divergence between the two personality systems.

Main Methods:

• Utilized an embedding-based mapping approach with miniLM sentence-transformers.
• Employed network psychometric methods to analyze personality data.
• Administered DM assessments and the IPIP-NEO 120 Big Five inventory to 185 participants.

Main Results:

• DM composites exhibited strong internal structure and high stability.
• Significant semantic-space alignment was found between DM and Big Five facet language.
• Modest person-level correlations (mean |r| ≈ 0.07) and facet-level associations (largest |r| ≈ 0.26) were observed.

Conclusions:

• Drawmetrics (DM) represents a coherent expressive-semantic trait space related to, but not identical with, Big Five traits.
• Findings support a linking interpretation rather than strict equivalence between DM and Big Five.
• Further research is needed on DM's scaling, reliability, range restriction, and criterion validation.