1. Designing the ideal political identity questionnaire using machine learning and ideology scalesAna Nikolić, Uroš Sergaš, Marko Tkalčič, 2025, objavljeni znanstveni prispevek na konferenci (vabljeno predavanje) Opis: 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.
Ključne besede: political ideology, questionnaire design, machine learning, psychometrics
Objavljeno v RUP: 30.01.2026; Ogledov: 165; Prenosov: 2
 Celotno besedilo (189,01 KB) |
2. Analiza vpliva tehnologije na kakovost spanja : zaključna nalogaTamara Mikač, 2025, diplomsko delo Ključne besede: kakovost spanja, modra svetloba, tehnologija, spalna rutina, življenski slog, napovedni modeli, stres, diplomske naloge
Objavljeno v RUP: 14.10.2025; Ogledov: 456; Prenosov: 51
Celotno besedilo (1,20 MB) |
3. |
4. |
5. |
6. |
7. |
8. |
9. |
10. Ranking footballers with multilevel modelingGregor Grbec, Nino Bašić, Marko Tkalčič, 2024, objavljeni znanstveni prispevek na konferenci Opis: Despite football’s collaborative nature, the inquiry into the identity of the best player is a frequent topic in the footballing realm. This discussion disproportionately highlights attacking players, creating an apparent bias, as every team role holds significance. Our study aimed to delineate player performance from team performance and ensure the inclusion of players from all positions in the ultimate ranking of the best players. We sourced data from FBref, encompassing every player in every match played by a top 20 European team in the current century’s top 5 European leagues. Employing a multilevel linear mixed-effects model, we utilized team points as the response variable, accounting for both player and opponent team strength. The extraction of level-2 player residuals, averaged by player, facilitated the creation of a comprehensive ranking for the best players of this century. Surprisingly, two players widely regarded as among the best of all time, Messi and Ronaldo, secured relatively low positions on our list (Ronaldo at 12th, and Messi at 14th).
Ključne besede: multilevel modeling, footballer ranking, sports modeling
Objavljeno v RUP: 05.06.2025; Ogledov: 808; Prenosov: 22
Celotno besedilo (211,09 KB)
Gradivo ima več datotek! Več... |
