Gamers represent themselves in online gaming worlds through their avatars. The term “Proteus Effect” (PE) defines the potential influences of the gamers’ avatars on their demeanour, perception and conduct and has been linked with excessive gaming. There is a significant lack of knowledge regarding likely distinct PE profiles and whether these could be differentially implicated with disordered gaming. A normative group of 1022 World of Warcraft (WoW) gamers were assessed in the present study (Mean age = 28.60 years). The Proteus Effect Scale (PES) was used to evaluate the possible avatar effect on gamers’ conduct, and the Internet Gaming Disorder Scale–Short-Form was used to examine gaming disorder behaviors. Latent class profiling resulted in three distinct PE classes, ‘non-influenced-gamers’ (NIGs), ‘perception-cognition-influenced-gamers’ (PCIGs), and ‘emotion-behaviour-influenced-gamers’ (EBIGs). The NIGs reported low rates across all PES items. The PCIGs indicated higher avatar influence in their perception-experience but did not report being affected emotionally. The EBIGs indicated significantly higher avatar influence in their emotion and behaviour than the other two classes but reported stability in their perception of aspects independent of their avatar. Gaming disorder behaviours were reduced for the NIGs and progressively increased for the PCIGs and the EBIGs.
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Cluster analysis was not preferred as despite providing information about different clusters of gamers, it does not provide information about the overall fit/applicability of the model and the exact chances of a specific gamer being classified into a certain category. Factor analysis was also not utilised as it refers to the extraction of dimensional-continuous latent factors and not categories-profiles, which was the aim here.
This test assesses the model with the number of typologies proposed against a model with one less typology (class). An insignificant Vuong-Lo-Mendell-Rubin test indicates that the assumed number of classes/typologies/profiles is necessary .
The AIC is regarded as an information theory goodness of fit measure—applicable when maximum likelihood estimation is used . This index is used to compare different models. Like the chi square index, the AIC also reflects the extent to which the observed and predicted covariance matrices differ from each other. Models that generate the lowest values are optimal.
The BIC is similar to the AIC expressing the log of a Bayes factor of the target model compared to the saturated model and penalises against complex models. Furthermore, a penalty against small samples is included in BIC calculation .
Entropy with values approaching 1 indicate clear delineation of classes .
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Stavropoulos, V., Pontes, H.M., Gomez, R. et al. Proteus Effect Profiles: how Do they Relate with Disordered Gaming Behaviours?. Psychiatr Q 91, 615–628 (2020). https://doi.org/10.1007/s11126-020-09727-4