Exploring L2 Engagement: A Large-Scale Survey of Secondary School Students

Abstract

Engagement, a psychological individual difference variable with three facets (vigour, dedication and absorption), has recently attracted scholarly attention. Through a large-scale survey, we examined what we call ‘L2 engagement’ among 21,370 secondary school students in China, with an L2 engagement scale adapted from the Utrecht Work Engagement Scale (UWES)-student version. Factor analysis showed this scale to be empirically unidimensional with three highly intercorrelated facets and very high internal consistency; this contributes to our understanding of the conceptual challenges surrounding the construct of engagement (e.g., dimensionality) and the broader issue concerning the correspondence between empirical constructs and theoretical terms (e.g., engagement in our case). Hierarchical regression revealed that the selected sociobiographical variables (e.g., L2 proficiency) were linked to L2 engagement to varying degrees; adopting a more refined approach to gauge the unique contribution of a predictor to L2 engagement in hierarchical regression, we identified L2 proficiency, parental attention, study time and frequency of parental coaching as (very) important predictors for L2 engagement. We call for more studies to adopt our L2 engagement scale, a sufficiently valid and reliable instrument developed based on a large sample. We also propose a few future research directions (e.g., combining self-reports with other data sources).

Is bilingualism linked to well-being? Evidence from a big-data survey

“…r = .1) in bivariate analyses. This criterion helps to ensure the principle of parsimony (Leech et al, 2014), which has also been adopted in some recent studies (e.g., Wang et al, 2022;Wei et al, 2022b).…”

“…This study identified a 'strong' association (r s = .34, p = .04) between bilingualism (operationalised as English proficiency) and well-being. The two studies reviewed above produced relatively large effect sizes (approaching the 'very large' benchmark of .30); however, these results probably over-estimated the strength of association between bilingualism and well-being because bivariate analyses (e.g., correlation) tend to generate inflated effect sizes (Wang et al, 2022;Wei et al, 2022a). One solution to this problem is to employ multivariate analyses that will generate a more comprehensive picture in terms of effect sizes.…”

“…Thirdly, although it is commendable that Zhang et al (2021) reported more than one type of effect size indexes (e.g., β and R 2 ), they only utilised βs in discussing the link between English proficiency and happiness. However, the effect size index β is inconducive to comparisons across different studies; in contrast, ΔR 2 is much more useful in cross-study comparisons (Wang et al, 2022). To overcome this particular limitation, one useful approach is calculating effect size ranges based on hierarchical regression analyses (Wei et al, 2020) and/or ranking the relative importance of each predictor in regression models via dominance analysis; the present studies endeavoured to attempt both methods (see Analytic Strategy for details).…”

“…addressed via hierarchical regression supplemented with dominance analysis. Hierarchical regression helps to ascertain the unique contribution of each IV to the DV-variance (Kong & Wei, 2019;Wei et al, 2020); the order for entering the IVs into regression models is of paramount importance because the DV-variance explained by each IV may vary significantly with the entry order (Wang et al, 2022;Wei et al, 2020); accordingly, researchers should attempt all possible entry orders and offer a range of effect sizes (rather than one single effect size) for each IV unless there are well-established theories to guide the (ideal) entry order (see Wang et al, 2022;Wei et al, 2022a). Dominance analysis helps to 'accurately determine predictor importance in multiple regression' (Mizumoto, 2023, p.195, emphasis added); this analysis generates the R 2 change which occurs when adding one IV to all possible subset regression models and hence identifies the contribution of the IV 'by itself and in combination with other predictors' (Tonidandel & Lebreton, 2011, p.2).…”

TESOL in the New Era: Introduction to the Special Column for the 2022 Global English Education China Assembly

“…correlation analysis and t-test) tend to generate inflated effect sizes compared with multivariate analyses (e.g. hierarchical regression) (Wei, Reynolds, Kong, & Liu, 2022;Wang et al, 2022). Given the multivariate nature of L2 learning, multivariate analyses paint a much more accurate picture than bivariate analyses (Wei, Wang & Liu, 2022;Wang et al, 2022).…”