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Designing the ideal political identity questionnaire using machine learning and ideology scalesAna Nikolić,
Uroš Sergaš,
Marko Tkalčič, 2025, published scientific conference contribution (invited lecture)
Abstract: Political ideology shapes beliefs, behavior and attitudes toward society. Many existing questionnaires for measuring ideology are lengthy, repetitive, and misaligned with self-perception. This paper investigates whether a shorter, reliable, two-dimensional political identity questionnaire can be created using machine learning and psychometric methods. Sixty participants completed four ideological instruments (MFQ, SDO, RWA and 8values). Lasso regression and Random Forest with nested cross-validation identified predictive items, while psychometric evaluation included CFA and Cronbach’s alpha. Random Forest outperformed Lasso. Internal reliability was excellent and factor loadings supported a two-factor structure despite moderate model it. Findings show that ideology can be measured efficiently with reduced items, supporting applications in research, digital platforms and political psychology.
Keywords: political ideology, questionnaire design, machine learning, psychometrics
Published in RUP: 30.01.2026; Views: 582; Downloads: 5
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