Grit across nations : the cross-national equivalence of the Grit-O Scale

Abstract

Despite its popularity in practice, the Grit-O Scale has shown inconsistent factorial structures and differing levels of internal consistency in samples outside the USA. The validity of the Grit-O Scale in different contexts is, therefore, questionable. As such, the purpose of this paper was to determine whether the Grit-O Scale could be used as a valid and reliable measure to compare grit across different nations. Specifically, the aim was to investigate the factorial validity, reliability, and concurrent validity of the Grit-O Scale and to investigate measurement invariance across three national cohorts (Europe, the USA, and Hong Kong). Data were gathered from 1888 respondents stemming from one USA- (n = 471), two Hong Kong- (n = 361) and four European (n = 1056) universities. A series of traditional CFA and less restrictive ESEM models were estimated and systematically compared to determine the best factorial form of the Grit-O Scale. The results showed that a bifactor ESEM model, with one general factor of overall grit and two specific factors (consistency of interest and perseverance of effort), fitted the data best, showed strong measurement invariance across the three samples, and showed itself to be a reliable measure. Furthermore, concurrent validity was established by showing that the three grit factors were directly and positively related to task performance. Meaningful latent comparisons between the three cultural cohorts could therefore be made. The results imply that cross-national comparisons of grit may only be problematic when traditional CFA approaches are favoured. In contrast, ESEM modelling approaches may compensate for cross-national differences in understanding grit and control for differences in the interpretation of the scale’s items. Therefore, the bifactor ESEM approach may be more appropriate for cross-cultural and cross-national comparison studies, as it allows for these differences to be meaningfully captured, modelled, and controlled for.