Psychometric Properties of Online Versions of Empathy And Dark Triad Personality Traits Questionnaires In Basketball Players




Abstract:
The goal of this study was to examine the psychometric properties of using the online version of self-report questionnaires for assessing empathy and dark triad personality traits to gather and analyse the data in scientific research. The assumption was made that, with the right written explanation and introduction to the questionnaire, the participants will fill out the online forms in the way that will revile good psychometric properties of the results. Sample for the study was composed out of 79 male athletes that actively played basketball in a 22/23 season. They filed an online version of the Sports Interpersonal Reactivity Index (IRI), and The Dark Triad Dirty Dozen (DTDD). Results of descriptive analyses reviled good and acceptable metric characteristics and discriminativeness of both questionnaires. Good reliability was confirmed with obtained Cronbach's alpha coefficient scores for DTDD but reliability of IRI scores was questionable. Obtained correlations among scales and subscales speak in favour of construct validity of online versions of questionnaires. It was concluded that further research of this topic is necessary but also justified.

CITATION:

IEEE format

P. Šešlija, N. Trunić, S. Marković, J. Popović, M. Milošević, “Psychometric Properties of Online Versions of Empathy And Dark Triad Personality Traits Questionnaires In Basketball Players,” in Sinteza 2023 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2023, pp. 259-266. doi:10.15308/Sinteza-2023-259-266

APA format

Šešlija, P., Trunić, N., Marković, S., Popović, J., Milošević, M. (2023). Psychometric Properties of Online Versions of Empathy And Dark Triad Personality Traits Questionnaires In Basketball Players. Paper presented at Sinteza 2023 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2023-259-266

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