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

Background: The Big Five personality inventory provides reliable and valid measures through its lexical self-report method. Drawmetrics (DM) is an expressive-semantic personality assessment that evaluates personality attributes through graphical drawings and their corresponding linguistic terms. Methods: Linguistic terms were used to derive psychometric scores for evaluating reliability, and structural, criterion, and convergent validity relative to the Big Five model. The Bidirectional Encoder Representations from Transformers (BERT) language model was used to process DM data for analysis. Analyses detected patterns in the data, which were then tested. The evaluation included the reliability of the α, ω, and ωₕ parameters, as well as network edge stability and consistency in centrality measurements. Results: The DM assessment achieved high internal consistency and a stable network structure. Network modularity analysis detected five domains that closely followed the Big Five personality trait structure. DM demonstrated convergent validity through high correlation coefficients, while discriminant validity established clear distinctions between DM and the Big Five. Structural validity estimates indicate that the DM domains align with the Big Five latent composites, and the criterion validity assessments confirm this alignment. Conclusion: DM demonstrated its psychometric viability by showing how people express their personality traits through nonverbal symbolic expressions.