Who becomes immersed and how? A multi-study synthesis of predictors and profiles of immersive engagement

Immersive experiences are widely studied, but the individual-difference antecedents that reliably predict them are not yet well integrated. This coordinated multi-study synthesis (Total N = 718 across six independent studies) addresses these inconsistencies by examining psychological predictors at both the total-score and subscale levels of the Immersive Tendencies Questionnaire (ITQ). A random-effects synthesis of 19 predictor–outcome effects on total ITQ scores yielded a small pooled effect (r = .110, p = .001). Experiential openness emerged as the sole consistent predictor (r = .25, I² = 0%), while emotional regulation, empathy and self-regulation appeared entirely non-significant. However, subscale decomposition revealed these apparent nulls were artefacts of statistical masking. Because traits like anxiety, depression and impulse control exerted significant but opposite-signed effects on the ITQ’s underlying Focus and Involvement dimensions, they largely cancelled during total-score aggregation. Because the ITQ does not readily conform to the assumptions of confirmatory factor analysis (CFA), a continuous principal component analysis (PCA) of ipsative profiles was used to circumvent simple-structure assumptions. This data-driven PCA independently recovered the theoretical Focus/Involvement distinction, with the extracted profile dimension tracking anxiety robustly across two samples (r = −.49, −.47; p < .001, FDR-corrected). Converging with hierarchical clustering and recent bifactor models, these findings suggest that openness predicts the overall level of immersive engagement, while anxiety, emotional reactivity and self-regulation shape its character. These findings highlight the importance of evaluating subscale-level effects in future cyberpsychology research.