Exploring Fuzzy Logic as an Alternative Approach in Psychological Scoring

Accurate measurement is foundational to psychological assessments, influencing research validity, clinical practice, and societal applications. While extensively utilized, traditional scoring methods suffer from limitations in granularity, sensitivity, and adaptability, potentially impacting the precision and utility of these assessments. This study aims to explore the efficacy of fuzzy logic as an alternative scoring method for the Satisfaction with Life Scale (SWLS), addressing these limitations.

A cross-sectional design involving 1,230 Greek adults was employed to compare traditional scoring methods with fuzzy logic. Data were collected online and analyzed using descriptive statistics, Pearson correlation, paired t-tests, regression analysis, and sensitivity analysis to evaluate the robustness and reliability of fuzzy logic scoring.

Results indicated that fuzzy logic scoring provides enhanced granularity and sensitivity, effectively capturing subtle variations in life satisfaction and mitigating ceiling and floor effects. The strong positive correlation (r = .9505) between traditional and fuzzy logic scores suggests high consistency. Significant mean differences highlighted the increased sensitivity of fuzzy logic scoring. The Bland-Altman plot confirmed good agreement between the methods, and sensitivity analysis demonstrated the robustness of fuzzy logic scoring across different parameter settings.

In conclusion, fuzzy logic offers a reliable and nuanced alternative to traditional scoring methods, significantly improving the accuracy and applicability of psychological assessments. Integrating fuzzy logic with advanced technologies could further enhance the precision and inclusivity of psychological evaluations, making it a promising tool for future psychological measurement.