Introduction
How foreign/second language (L2) learners’ cognitive features, psychological attributes, and other individual differences (IDs) affect target language learning have long intrigued researchers from applied linguistics in general and bilingualism research in particular (Luo & Wei, Reference Luo and Wei2021). In the past five decades, much research has investigated a variety of learners’ IDs, cognitive and non-cognitive (e.g., psychological) (Kong & Wei, Reference Kong and Wei2019; Oxford & Ehrman, Reference Oxford and Ehrman1993; Pimsleur, Reference Pimsleur1966; Robinson, Reference Robinson2002; Rubin, Reference Rubin1975; Skehan, Reference Skehan1989). Psychological variables (e.g., resilience, see Wei et al., Reference Wei, Wang and Li2022b) represent an under-researched sub-category of IDs vis-a-vis their cognitive counterparts, such as working memory (Wen & Skehan, Reference Wen and Skehan2021) and aptitude (Li & Zhao, Reference Li and Zhao2021); accordingly, recent calls for more research attention on the former ID sub-category have been made (Luo & Wei, Reference Luo and Wei2021; Walton, Reference Walton2022; Wei & Gao, Reference Wei and Gao2022).
In the field of bilingualism, there are two different lines of research on psychological IDs (Luo & Wei, Reference Luo and Wei2021). The first line has its roots in theoretical models (e.g., Baker, Reference Baker1996; Gardner, Reference Gardner1985) that tend to regard IDs as the independent variables (IVs) and language proficiency variables as the dependent ones. In contrast, the second line of inquiry has attracted scholarly attention since the late 2000s (e.g., Dewaele, Reference Dewaele, Sarch, Stephen and Marion2012; Dewaele & van Oudenhoven, Reference Dewaele and van Oudenhoven2009); studies along this newer research line (e.g., Grey & Thomas, Reference Grey and Thomas2019; Wei & Hu, Reference Wei and Hu2019) tend to treat IDs as the dependent variables (DVs), with language proficiency variables being the independent ones. The present study, focussing on the psychological ID of well-being, contributes to the second research line.
Well-being, conceptualised as a psychological ID (Sari et al., Reference Sari, Chasiotis, van de Vijver and Bender2018), refers to the psychological state where a person ‘subjectively believes his or her life is desirable, pleasant, and good’ (Diener, Reference Diener2009, p.1). The present study represents one of the very few attempts to examine this psychological ID among a nationally representative sample of bilingualsFootnote 1 from a big-data survey. Several recent studies (e.g., Kang, Reference Kang2022) have attempted to explore the impact of language on well-being, generating valuable insights such as 'language makes life better’ (Zhang & Cheng, Reference Zhang and Cheng2022). However, as applied linguistics researchers, we need to probe further: higher proficiency in which language (e.g., a first language? a foreign language (FL)?) makes life better (viz. leading to a higher level of well-being) when it comes to bilinguals? This big question is particularly important, as bilingualism is the norm in most regions of today's world including the People's Republic of China (henceforth ‘China’). As will be shown below, extant studies about the potential link Footnote 2 between language and well-being have unfortunately ignored foreign language(s) and focused exclusively on the Chinese language. The present study endeavours to narrow this research gap.
The present study is the first systematic attempt to link language with well-being based on a nationally representative sample from China by considering both the national language and English (a FL). English is more relevant to the post-reform generation (i.e., people born in 1978 or later) in China (Wei & Su, Reference Wei and Su2008; Wen & Zhang, Reference Wen, Zhang and Tsui2020). Accordingly, our study focuses upon the respondents belonging to this generation (N = 3471) from a recent wave (2017) of the Chinese General Social Survey (CGSS), which utilised a representative national sample (N = 12,582).
Besides adding new knowledge to well-being, an under-investigated psychological ID, our study contributes to four further areas. First, in the current big-data era, any research effort to utilise representative samples, especially samples from big-data surveys readily available in the public domain, would be most valuable (Wei et al., Reference Wei, Reynolds, Kong and Liu2022a). Specifically speaking, while Zhang and Cheng (Reference Zhang and Cheng2022) attempted to link language with well-being based on data from the CGSS (a big-data survey), replications (especially with an improved design) are needed because the value of replication research (Marsden et al., Reference Marsden, Morgan-Short, Thompson and Abugaber2018) is increasingly recognised in applied linguistics generally and bilingualism research in particular. Second, we advocate a more holistic perspective towards language (including the national language and FL) in any bilingual context. This advocacy for broadening the research scope for the link between language and a focal psychological ID (e.g., well-being) will prove to be worthwhile (see our analysis below). Third, our study can paint a more comprehensive picture of the influence of bilingualism and other sociobiographical factors on well-being by adopting a more refined approach based on hierarchical regression (Wei et al., Reference Wei, Liu and Wang2020) supplemented by dominance analysis (Mizumoto, Reference Mizumoto2023). Fourth, this study increases our understanding of the psychological profiles of bilinguals in China, an under-examined English as a foreign language (EFL) context (Wei & Hu, Reference Wei and Hu2019), where there are more than 390 million English-knowing bilinguals (Wei & Gao, Reference Wei and Gao2022).
Literature Review
Psychological effects of bilingualism
In the past decade, much research has explored the effect of bilingualism on different psychological IDs, be they negative (e.g., anxiety, see Jiang & Dewaele, Reference Jiang and Dewaele2020), neutral (e.g., extroversion, see Chen et al., Reference Chen, Dies, Uni and Mu2015), or positive (e.g., L2 grit, see Wei et al., Reference Wei, Liu and Wang2020). Most of the relevant studies are primarily quantitative; when reviewing quantitative research, we focus on effect size, which is more important than the statistical significance level (p) (Wei & Gao, Reference Wei and Gao2022; Wei et al., Reference Wei, Jiang and Kong2021).
Several studies (e.g., Dewaele & MacIntyre, Reference Dewaele and MacIntyre2014) have explored the influence of bilingualism on foreign language enjoyment (FLE), a positive psychological ID similar to our focal variable, well-being (Wang et al., Reference Wang, Derakhshan and Zhang2021). For instance, based on a sample of 189 high school students in Greater London, Dewaele et al. (Reference Dewaele, Witney, Saito and Dewaele2018, p. 684) found (1) number of languages known was ‘unrelated’ to FLE (F(5, 185) = 1.6, no effect sizes reported, p > .05); and (2) level in the FL (operationalised as self-evaluation on a four-point scale) had a statistically significant effect on FLE (F(3, 185) = 4.4, p < .005, eta2 = .066, Cohen's d = .53). While it was commendable that these researchers provide more than one effect size measure for the focal effect, unfortunately they did not provide effect sizes for statistically non-significant results; in contrast, the present study will report effect sizes for both statically significant and non-significant results following the recommended statistical practices (Larson-Hall & Plonsky, Reference Larson-Hall and Plonsky2015).
Some studies (e.g., Dewaele & van Oudenhoven, Reference Dewaele and van Oudenhoven2009) have explored the influence of bilingualism on tolerance of ambiguity (TA). For instance, Chen's (Reference Chen2004) survey of 193 EFL students in China found that TA was statistically significantly correlated with their English proficiency (a measure of bilingualism) (r = .407, p < .001). A more recent study in the Chinese EFL context is Wei and Hu's (Reference Wei and Hu2019) survey of 260 English-using bilinguals; an important finding was that bilingualism accounted for 1.4% of the TA variance.
Several recent studies (e.g., Liu & Wang, Reference Liu and Wang2021) have examined the influence of bilingualism on grit, another positive ID variable similar to well-being. Khajavy and Aghaee (Reference Khajavy and Aghaee2022) focussed on perseverance of effort and consistency of interest – namely, two components of grit, in 226 EFL learners at one private language institute in Iran; one major finding was that perseverance of effort was positively correlated with English proficiency (operationalised as final scores at the end of the semester in the school-based test) (r = .16, p = .014). Based on a sample of 462 Chinese EFL learners, Wei et al. (Reference Wei, Liu and Wang2020) found that self-rated English proficiency (a measure of bilingualism) could explain up to 3.9% of the variance in L2 grit, with the effect size range being 1.1%–3.9% in their hierarchical regression models. These researchers utilised Wei and Hu's (Reference Wei and Hu2019) effect size benchmark system, where .5%, 1%, 2%, and 9% respectively represent the small, typical (medium), large, and very large cut-offs for the effect size measure R2.Footnote 3 Regarding the influence of bilingualism, its effect size upper limit (3.9%) exceeded the ‘large’ benchmark and its lower limit (1.1%) exceeded the ‘typical’ one; hence the effect of bilingualism on L2 grit was deemed important by Wei et al. (Reference Wei, Liu and Wang2020). Wei and Hu's (Reference Wei and Hu2019) benchmark system, which has been adopted for effect size interpretation in recent studies (e.g., Dewaele & Botes, Reference Dewaele and Botes2020), will also be adopted in the present study.
The effect of bilingualism on well-being
In connection with learning in general, the effect of learning on well-being has been widely discussed both theoretically (e.g., Desjardins, Reference Desjardins2008; Wang & Wang, Reference Wang and Wang2008) and empirically (e.g., Cuñado & de Gracia, Reference Cuñado and de Gracia2012). As regards language learning in particular, FL learning that leads to the development of bilingualism can become ‘a source of well-being’ (Proietti Ergün & Ersöz Demirdağ, Reference Proietti Ergün and Ersöz Demirdağ2022, p.3). Put differently, bilingualism can exert influence on well-being (to varying degrees in different contexts), as will be illustrated by the studies reviewed below.
Two important lines of inquiry concerning bilingualism and well-being merit attention. The first line of research has a particular focus upon minority groups, such as immigrants (e.g., Kim et al., Reference Kim, Ehrich and Ficorilli2012; Lee et al., Reference Lee, Niu and Yang2021) and minority ethnicities (e.g., Shields & Price, Reference Shields and Price2002) in a particular polity. The other line of inquiry that is emerging does not focus exclusively on minority groups, which will be delineated below.
Grey and Thomas (Reference Grey and Thomas2019) investigated the link between bilingualism (operationalised as language proficiency) and well-being among 210 female citizens of the United Arab Emirates. The language proficiency variable was a self-report measure, with a higher score indicating a higher level of Arabic language proficiency. Well-being was gauged with the World Health Organization's well-being index. This cross-sectional correlational study identified a nearly ‘very strong’ association (r = .26, p < .01) between language proficiency and well-being.
Kim et al. (Reference Kim, Ehrich and Ficorilli2012) explored the settlement experiences of 46 adult immigrant learners of English from three different first language (L1) backgrounds: Chinese, Arabic, and Vietnamese. All these learners were undertaking English language study in one English programme for adult migrants at the time of investigation. The data, which comprised audio-recordings, transcripts, and field notes of semi-structured interviews, were collected over a one-year period. On the one hand, the researchers rated the participants’ English proficiency (an indicator of bilingualism) as the ability to speak this additional language in the semi-structured interviews on a nine-point scale according to the IELTS criteria. On the other hand, these researchers evaluated the participants’ perception of well-being by assigning one of the three scores—1 being low, 2 medium, and 3 high—to each selected interview by scrutinising its entire transcript, its field notes, and the socio-biographical profile for the participant in question. This study identified a ‘strong’ association (rs = .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., Reference Wang, Ying, Liu and Wei2022; Wei et al., Reference Wei, Reynolds, Kong and Liu2022a). One solution to this problem is to employ multivariate analyses that will generate a more comprehensive picture in terms of effect sizes.
Although most studies used (simple) bivariate analyses, some recent studies have started to employ multivariate analyses that will generate a more comprehensive picture. For instance, employing hierarchical regression, Khawaja et al. (Reference Khawaja, Yang and Cockshaw2016) investigated the factors (e.g., English proficiency) affecting migrants’ well-being among Chinese-speaking Taiwanese migrants who settled in Australia. The participants (N = 271) completed a questionnaire battery available in both Chinese and English. Well-being was measured by the Satisfaction with Life Scale (SWLS) (Diener et al., Reference Diener, Emmons, Larsen, Griffin, Diener, Emmons, Larsen and Griffin1985), a five-item instrument on a seven-point Likert-scale (a higher score indicating a high level of well-being). English language proficiency (a measure of bilingualism) was self-rated by the participant (scale unreported). Results indicated that age (β = .131, p = .001), years of residence (β = .222, p = .004), social support (β = .440, p = .288), and bilingualism (β = .104, p = .444) were positively linked with well-being, accounting for 17% of the well-being variance. Although these authors reported the conventional effect size β, which is largely unconducive to cross-study comparisons, it would be optimal if two or more types of effect sizes (e.g., ΔR2 for regression) could be reported (Luo & Wei, Reference Luo and Wei2021) so as to facilitate comparisons across studies. To overcome this limitation, in our regression analyses below, we reported the crucial effect size index ΔR2, which can facilitate cross-study comparisons, along with the conventional indexes (B and β).
Drawing on the AsiaBarometer Surveys 2006 and 2007, the primary part of Zhang et al. (Reference Zhang, Dai and Liu2021) investigated the effect of English proficiency (a measure of bilingualism) on subjective well-being; the secondary part of their study was a robustness check ‘conducted with an alternative dataset targeted at a specific country among the 14 countries or regions’ (p. 12). Their sample comprised 14,811 respondents in 14 East and Southeast Asian countries or regions, including China. These researchers used hierarchal regression to control for six sociobiographical variables (e.g., gender) and ascertain the effect of English proficiency on well-being. They found that (1) English proficiency explained .9% of the well-being variance (β = .127), and (2) the six control variables altogether accounted for 3.3% of the well-being variance. Finding (2) painted a vague picture of the contribution of each of these six predictors to the variance in well-being, which could be improved by Wei et al.'s (Reference Wei, Liu and Wang2020) more refined version of hierarchical regression that will be adopted by the present study. Notwithstanding this limitation, it is commendable that Zhang et al. (Reference Zhang, Dai and Liu2021) reported more than two types of effect size indexes (e.g., β and R2), among which ΔR2 is most conducive for cross-study comparisons as mentioned above. As will be seen below, Finding (1), which suggested that English proficiency was positively linked to well-being at a level slightly below the typical benchmark (1%), will be usefully compared with results from the present study.
In the Chinese context, a handful of studies have explored the effect of language variables on well-being (e.g., Kang, Reference Kang2022). Two studies are most relevant. One is the secondary part of Zhang et al.'s (Reference Zhang, Dai and Liu2021) research reviewed above, which used the 2017 CGSS data for the robustness check (see their Table 5). Five control variables (gender, age, marital status, education, and socio-economic status (SES)), two IVs (English listening proficiency and English speaking proficiency), one mediator (income satisfaction), and one DV (happinessFootnote 4) from merely 4,032 participants were used in regression analyses ‘after screening the missing and abnormal values of the selected variables’ (p. 12). Some interesting findings were: (1) English listening proficiency was statistically significantly correlated with happiness (β = .063, p < .01) and the explanatory power of this language variable was weaker than that of SES (β = .201, p < .01 see Model 11 in their Table 5); (2) English speaking proficiency was also statistically linked with happiness (β = .062, p < .01) and again the explanatory power of this language variable was weaker than that of SES (β = .201, p < .01, see Model 14 in their Table 5).
Despite their use of two effect size measures (e.g., β and R2), three major limitations remain. Firstly, a prerequisite for mediation analyses is that the links respectively between the IV, DV, and mediator are established (Agler & Boeck, Reference Agler and Boeck2017; Judd & Kenny, Reference Judd and Kenny1981)Footnote 5. Unfortunately, Zhang et al. (Reference Zhang, Dai and Liu2021) failed to take into account this prerequisite; it appears that they rushed to apply mediation analyses before establishing the link between IV (English proficiency) and DV (well-being)Footnote 6. Secondly, although the valid questionnaires in the 2017 CGSS totalled 12,580, only 4,032 were used in the second part of Zhang et al. (Reference Zhang, Dai and Liu2021), who failed to account for the information from 8450 questionnaires (61.17%, over two-thirds of the original sample)Footnote 7. This practice was problematic because it incurred significant information loss and cast down on the trustworthiness of the design of the relevant itemFootnote 8. Thirdly, although it is commendable that Zhang et al. (Reference Zhang, Dai and Liu2021) reported more than one type of effect size indexes (e.g., β and R2), 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, ΔR2 is much more useful in cross-study comparisons (Wang et al., Reference Wang, Ying, Liu and Wei2022). To overcome this particular limitation, one useful approach is calculating effect size ranges based on hierarchical regression analyses (Wei et al., Reference Wei, Liu and Wang2020) 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).
The other most relevant study is Zhang and Cheng's (Reference Zhang and Cheng2022) paper entitled ‘Language Makes Life Better: The Impact of Mandarin ProficiencyFootnote 9 on Residents’ Subjective Well-Being’ that drew upon two earlier waves (2012 and 2015) of the CGSS. As the title suggested, this study focused on Putonghua proficiency, although it aspired to respond to the big question ‘is language linked to well-being’. It turned out that these researchers only used Putonghua proficiency data from the CGSS surveys despite the availability of English proficiency data. To address this limitation, the present study, which draws upon the 2017 CGSS, will not confine language data to the national language only. Put differently, our study endeavours to address the (potential) role of FL(s) in the data collection processFootnote 10.
A most relevant finding from Zhang and Cheng (Reference Zhang and Cheng2022) was that Putonghua proficiency was a statistically significant predictor for well-being. However, it was only reported that the language variable together with the other 13 non-language predictors accounted for (at most) 19.8% and 20.4% in the variance of well-being respectively for the 2012 and 2015 waves. Such reporting practice reflected one limitation with data analysis, which was also identified in Zhang et al.'s (Reference Zhang, Dai and Liu2021) study reviewed above: Zhang and Cheng (Reference Zhang and Cheng2022) unfortunately did not attempt to ascertain the unique contribution of the predictors (e.g., Putonghua proficiency) to the well-being variance. To overcome this limitation with the data analysis process, our study adopts Wei et al.'s (Reference Wei, Liu and Wang2020) more refined version of hierarchical regression that helps to gauge each IV's unique contribution.
The links between other sociobiographical variables and well-being
Our study aims to ascertain the extent to which bilingualism, vis-à-vis other sociobiographical variables, is linked to well-being.
As indicated above, in a recent study most relevant to ours, Zhang and Cheng (Reference Zhang and Cheng2022) examined the influence from up to 13 non-language sociobiographical variables (e.g., SES) on well-being. As indicated above, these 13 predictors altogether explained about 20% of the well-being variance in the 2012 and 2015 CGSS waves. Besides this result concerning effect size, three major findings concerning statistical significance included (1) in both CGSS waves, gender, healthFootnote 11, marital status, perceived social fairness, SES and income emerged as statistically significant predictors (p < .01) of well-being consistently; (2) in both waves, ethnicity and resident type turned out to be statistically non-significant predictors; and (3) age, years of education, religion and household registration were unstable predictors as the statistical significance for their prediction was not consistent in both waves. As our study also drew upon the CGSS, all of the non-language predictors in Zhang and Cheng (Reference Zhang and Cheng2022) were considered in the analysis below.
The Present Study
Research questions (RQs)
RQ1: To what extent does bilingualism (operationalised as English proficiency) predict well-being?
RQ2: To what extent does bilingualism, vis-à-vis the selected sociobiographical variables (e.g., SES), predict well-being?
Data
The data source for the present study was the 2017 wave of the CGSS, which was the latest available at the time of writing. The CGSS is ‘the first’ continuous survey project run by academic institutions in the Chinese mainland (Hu & Li, Reference Hu and Li2019, p. 156). It draws on a nationally representative sample and collects data at the multiple levels of society, community, family, and individual. Although it was described as ‘an annual survey started since 2003’ (Chen et al., Reference Chen, Shen, Liang and Guo2021, p. 2), in the past few years it has been conducted every two years; for instance, following the 2015 wave (see e.g., Luo & Wei, Reference Luo and Wei2021), the 2017 wave of CGSS released the collated data to the public in October 2020, whereas the data of the next wave were not released prior to the submission of our paper for peer review. We suggest that using the 2017 wave of CGSS be useful because of two majorFootnote 12 considerations. First, our ‘prototype paper’ (i.e., the paper being replicated), Zhang and Cheng (Reference Zhang and Cheng2022) utilised two earlier waves (2012 and 2015) of the same big-data survey. Second, although the secondary (and less important) part of Zhang et al. (Reference Zhang, Dai and Liu2021) utilised the 2017 CGSS only to some extent, the primary part of Zhang et al. (Reference Zhang, Dai and Liu2021) utilised the AsiaBarometer Surveys 2006 and 2007. Put differently, the 2017 CGSS wave was used in a supplementary way to support Zhang et al.'s (Reference Zhang, Dai and Liu2021) focus on another big-data survey (similar to the CGSS) conducted over 10 years ago. Conducted through individual interviews and structured questionnaire surveys (Liu et al., Reference Liu, Cao, Nie, Wang, Tian and Ma2021), the CGSS endeavours to systematically monitor the changing relationships between social structure and quality of life in urban and rural China (Luo & Wei, Reference Luo and Wei2021). Adopting a random sampling methodFootnote 13, the 2017 CGSS questionnaire solicits essential information on more than 700 variables including well-being, English proficiency (a measure of bilingualism), Putonghua proficiency, and a wide range of sociobiographical characteristics (e.g., SES) from respondents. The 2017 CGSS national sample comprised 12,582 Chinese citizens aged 18 or above. Following scholars who examine Chinese generations based on key historical events (Egri & Ralston, Reference Egri and Ralston2004; Tang et al., Reference Tang, Wang and Zhang2017), we defined the group of people born in 1978 or later as the post-reform generation as the year 1978 was regarded as the beginning of China's modernization (Lu & Alon, Reference Lu and Alon2004; Tang et al., Reference Tang, Wang and Zhang2017). For the purpose of the present study, the focal sample was confined to the respondents born in 1978 or later (N = 3471) in the above 2017 CGSS national sample.
Dependent variable
The outcome variable well-beingFootnote 14 was measured by one item: ‘Do you think you live a happy life?’ Responses were originally coded on a five-point Likert scale ranging from 1 to 5, where 1 indicated ‘very unhappy’ and 5 ‘very happy’. A higher score reflected a higher level of well-being.
This single-item measure has both advantages and disadvantages. But overall speaking, it is ‘reliable, valid, and viable’ in survey-based studies (Abdel-Khalek, Reference Abdel-Khalek2006, p.129). The single-item measurement for well-being has been adopted in a series of analyses based on the CGSS (e.g., Ding et al., Reference Ding, Salinas-Jiménez and Salinas-Jiménez2021; Qi et al., Reference Qi, Xu, Qi and Sun2023); most recently, Yan et al. (Reference Yan, Deng, Igartua and Song2023) reported that the single question on well-being from the 2017 CGSS is ‘reliable and effective’ (p. 4) in the Chinese context.
Independent variables
There were 14 initial IVs in the present study. These 14 variables were selected for two major reasons. First, the present study was exploratory in nature in that ‘no previous study has ever focused on the English-well-being linkage’ (Zhang et al., Reference Zhang, Dai and Liu2021, p.2). Any exploratory study should prioritise selecting factors, which should not incur significant information loss, from a myriad of potential influencing factors; specifically, the present study managed to avoid selecting some variables (e.g., income satisfaction, see the critique of Zhang et al., (Reference Zhang, Dai and Liu2021) in the Literature Review section) that unfortunately caused serious information loss from the 700+ variables covered by the CGSS). Second, the present study represented a replication of Zhang and Cheng (Reference Zhang and Cheng2022). A common strategy for variable selection in any replication study is to retain all (if not most) of the IVs from the prototype paper for our (partial) replication); hence our study chose to (1) keep all of the 14 IVs Footnote 15 from Zhang and Cheng (Reference Zhang and Cheng2022) and (2) add another factor (English proficiency) because of our adoption of a more holistic perspective towards language.
Two were our focal IVs (viz. factors of our main interest): English proficiency and Putonghua proficiency. The two items (on a 5-point Likert scale) respectively measuring the respondent's proficiency in English listening and speaking were added up to indicate the overall English proficiency; this newly created variable was a proxy for bilingualism, with a higher score reflecting a higher level of bilingualism. Similarly, the second focal variable, Putonghua proficiency, was measured by adding up the scores from the respondents’ ratings on a 5-point Likert scale of their proficiency in both Putonghua listening and speaking; again, a higher score of this newly created variable indicated a higher Putonghua proficiency level.
The other 12 IVs (see Table 1), which might affect well-being (see the Literature Review section), were also considered to facilitate comparison with previous research (e.g., Zhang & Cheng, Reference Zhang and Cheng2022). For instance, SES was assessed with a five-point Likert scale in response to the question; ‘in your opinion, which SES does your family belong to’ (1 = ‘far below the average level of SES’ and 5 = ‘far above the average level of SES’); the higher the score, the higher the SES; this measure of SES was the same as used in previous studies (e.g., Zhang et al., Reference Zhang, Dai and Liu2021; Zhang & Cheng, Reference Zhang and Cheng2022).
Table 1. Participant Profile
Note: Following Chen et al. (Reference Chen, Shen, Liang and Guo2021), we took the logarithm of ‘income’ and generated a new variable called ‘Ln income’ to reduce collinearity.
Analytic strategy
RQ1, which explored the association between bilingualism and well-being, was dealt with via simple linear regression. RQ2 examining the influence of the 14 initial IVs on well-being was 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, Reference Kong and Wei2019; Wei et al., Reference Wei, Liu and Wang2020); 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., Reference Wang, Ying, Liu and Wei2022; Wei et al., Reference Wei, Liu and Wang2020); 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., Reference Wang, Ying, Liu and Wei2022; Wei et al., Reference Wei, Reynolds, Kong and Liu2022a). Dominance analysis helps to ‘accurately determine predictor importance in multiple regression’ (Mizumoto, Reference Mizumoto2023, p.195, emphasis added); this analysis generates the R2 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, Reference Tonidandel and Lebreton2011, p.2). In this analysis, the average of R2 change produced in all possible subsets is called dominance weight; the sum of dominance weights is equal to the total DV-variance-explained (reflected by the effect size R2) (see Table S2 in Supplementary Material). A reader-friendly version of dominance weights is called rescaled weights; the rescaled dominance weight for each IV reflects the percentage of the DV-variance it contributes to the total DV-variance-explained, and the rescaled values add up to 100% (see Table 4).
We conducted all the statistical procedures (except for dominance analysis) with SPSS 27; the supplementary dominance analysis was performed via a web application developed by Fan (Reference Fan2023). In language-related disciplines, it is only very recently that researchers become increasingly aware of dominance analysis as a viable alternative for estimating predictor importance in regression models (Mizumoto, Reference Mizumoto2023).
For the sake of brevity, in what follows, we set the statistical significance level at the conventional cut-off (α = .05, non-directional). Following the recommended practice of statistics reporting (Sun et al., Reference Sun, Pan and Wang2010; Wei et al., Reference Wei, Hu and Xiong2019), we reported exact p values (with very small ps being reported as p < .0005).
Results
RQ1. The link between bilingualism and well-being
A simple linear regression analysis, with bilingualism as the IV and well-being as the DV, generated a statistically significant model (R2 = .026, adjusted R2 = .026, F = 91.779, p < .0005), suggesting that bilingualism (i.e., English proficiency) explained 2.6% of the well-being variance. The effect size exceeded the ‘strong’ benchmark (2%) in Wei and Hu's (Reference Wei and Hu2019) effect size interpretation system.
RQ2. The influence of the selected sociobiographical variables on well-being
Prior to running hierarchical regression, we conducted two rounds of data checking. The first round was a preliminary analysis, aiming to ascertain which of the initial 14 IVs could be included into regression analysis. The inclusion criterion was that the strength of the association between a predictor and well-being should be stronger than the typical benchmark (viz. r = .1) in bivariate analyses. This criterion helps to ensure the principle of parsimony (Leech et al., Reference Leech, C. Barret and Morgan2014), which has also been adopted in some recent studies (e.g., Wang et al., Reference Wang, Ying, Liu and Wei2022; Wei et al., Reference Wei, Wang and Li2022b).
The preliminary analysis that consisted of several rounds of bivariate analyses confirmed that the eight italicised variables in Table 2 were suitable for inclusion into regression analysis. Specifically, seven correlation analyses yielded the effect sizes (in descending order, see Table 2) for the links between the non-binary variables and well-being; one independent-samples t-test revealed the statistically significant difference (p < .0005) in well-being between respondents with rural household registration and those with non-rural household registration (r = .110, slightly exceeding the ‘typical’ benchmark); the latter (M = 3.99, SD = .71, N = 1635) had a higher level of well-being than their counterparts (M = 3.82, SD = .84, N = 1818).
Table 2. Links between the 14 initial independent variables and well-being
Note: * indicates p < .0005. Three digits numbers following the decimal point are kept except for the need to reveal a more nuanced difference.
The second round of data checking was conducted to ensure that the assumptions (e.g., normality and homoscedasticity) for regression were met. For example, when checking for potential outliers, we conducted several rounds of casewise diagnostic analyses and deleted 53 cases that had a standardized residual greater than 3 or smaller than –3. Hence the initial sample size (N = 3471) was slightly reduced to 3418.
Then we used this revised sample in a series of hierarchical regression analyses with the eight italicised variables (see Table 2) as predictors. Each predictor was entered, one by one, into each of the eight models (or ‘blocks’ as called in SPSS). A total of eight predictors will generate 40,320 (8 × 7x6 × 5x4 × 3x2 × 1) possible entry orders and hence produce up to 40,320 different scenarios (see Table 3 for one sample scenario); put differently, for each predictor, there could be 40,320 different effect size values. All in all, our hierarchical regression analyses generated two crucial findings.
Table 3. Hierarchical regression predicting well-being: Model Summary
Note: For Models 1-8, the variable underneath ‘Model’ indicates that it was the newly added predictor in this particular model.
The first finding was that the eight predictors, regardless of the entry orders, explained a total of 11.4% in the well-being variance (R2 = .117). The second crucial finding included the ranges of the effect size △R2 for the eight predictors: perceived social fairness (3.29 - 4.58%), SES (2.08 – 4.86%), health (1.60 – 3.65%), English proficiency (.24 – 2.41%), Putonghua proficiency (.23 – 2.15%), years of education (.03 – 1.68%), income (.01 – 1.18%), and household registration (.03 – .95%). Regarding the first and second predictors, their maximum and minimum effect sizes exceeded the large benchmark (2%), suggesting that they are very important predictors. Regarding the second and third predictors, SES and health, their effect size minimums exceeded the typical benchmark (1%) and their maximums the large benchmark (2%), indicating that they were important predictors. Regarding English proficiency and Putonghua proficiency, their effect size maximums also exceeded the large benchmark (2%), although their effect size minimums dropped below the small benchmark (.5%); this meant that they could be important predictors for well-being. Regarding the last three predictors, their effect size minimums fell below the small benchmark (.5%), and at the same time their effect size maximums did not exceed the large benchmark (2%), suggesting that they might exert negligible effect on well-being and hence were relatively unimportant. Furthermore, these crucial results (e.g., effect size ranges) were confirmed with dominance analysis (see the last column in Table 4). Additionally, the results of dominance analysis were visualized in Figure S1 of Supplementary Material.
Table 4. Predictor importance
Note: Fairness = perceived social fairness; English = English proficiency; Putonghua = Putonghua proficiency; YoEdu = Years of education; HouReg = Household registration
Table 3 provides the key information of one example from the 40,000+ regression scenarios predicting well-being. In this scenario, English proficiency was entered into the first block, Putonghua proficiency the second, years of education the third, resident type the fourth, income the fifth, SES the sixth, health the seventh, and perceived social fairness the eighth. Each block statistically significantly (p < .0005) added to the prediction of well-being; in Model 8 (see Table 3), the eight predictors altogether accounted for 11.4% of the variance in well-being (R2 = .117). The △R2 column in Table 3 contains the most important information: (1) English proficiency, SES and perceived social fairness respectively explained 2.4%, 3.0% and 3.3% of the well-being variance, which exceeded the large effect size benchmark (2%), (2) health accounted for 1.8% of the well-being variance, which was higher than the typical effect size benchmark (1%), (3) Putonghua proficiency contributed 0.8% in the well-being variance, which exceeded the small effect size benchmark (0.5%), and (4) the unique contribution to the well-being variance respectively from years of education (.1%), household registration (.0%), and ln income (.3%) fell below the small effect size benchmark (.5%) and hence could be deemed negligible. It was noteworthy that in this particular regression scenario English proficiency emerged as a more important predictor for well-being than Putonghua proficiency.
Discussion
RQ1 examines to what extent bilingualism is linked to well-being. A concise answer to RQ1 is that higher bilingualism was statistically significantly associated with a higher well-being level and the strength of this association r = .16 (i.e., the unsquared version of R2 = .026) slightly exceeded the strong benchmark (.14). The former part of our answer concerning statistical significance echoed previous studies (e.g., Grey & Thomas, Reference Grey and Thomas2019; Kim et al., Reference Kim, Ehrich and Ficorilli2012). Similarly, the latter part of our answer concerning the strength of association fell within the range of effect sizes identified in earlier research; specifically, our effect size (.16) was higher than Zhang et al.'s (Reference Zhang, Dai and Liu2021) result (.09) and lower than Grey and Thomas’ (Reference Grey and Thomas2019) finding (.26).
The mechanism behind this link between bilingualism and well-being (an indicator of a better life) may be attributed to both (language) learner external (e.g., job requirements) and internal factors (e.g., an open and curious attitude towards the world, see Luo & Wei, Reference Luo and Wei2021). For example, in China, an adult with FL-based bilingualism (Wei et al., Reference Wei, Reynolds, Kong and Liu2022a) manages to find a highly-paid job because of his/her higher English language proficiency compared with the other competitors, which leads to a higher level of well-being. We need to acknowledge that there may be alternative explanations and applying mediator analyses could be one strategy in future research to ascertain the mechanism behind the bilingualism-well-being linkage. However, the current state of knowledge does not warrant the application of mediation analyses; more empirical investigations are required to establish the extent of the bilingualism-well-being linkage before meaningful mediation analyses are conducted (see also Note 5).
As results from bivariate analyses (e.g., simple linear regression) tend to produce inflated effect sizes (Wang et al., Reference Wang, Ying, Liu and Wei2022; Wei et al., Reference Wei, Reynolds, Kong and Liu2022a), more attention should be paid to the discussion of RQ2 below, which was addressed by multivariate analyses (e.g., hierarchical regression) that could paint a more accurate picture than bivariate analyses.
RQ2 probes the extents to which bilingualism and other selected variables were linked to well-being. Our succinct answer to RQ2 is that perceived social fairness (effect size ΔR2 = 3.4 – 4.7%), SES (2.08 – 4.86%), and health (1.60 – 3.65%) were important predictors for well-being, while English proficiency (0.3 – 2.5%) and Putonghua proficiency (0.2 – 2.0%) were potentially important predictors. Three crucial observations can be made here. Firstly, in terms of statistical significance, our results (e.g., see Model 8 in Table S1 of Supplementary Material) were consistent with previous studies (e.g., Zhang et al., Reference Zhang, Dai and Liu2021); specifically, for instance, perceived social fairness, health, SES, and Putonghua proficiency were statistically significant predictors for well-being, as were the case in Zhang and Cheng (Reference Zhang and Cheng2022). Secondly, the effect size ranges from the present study cannot be compared with previous findings, partly because researchers using multiple regression tended to rely only upon one effect size for the contribution of each predictor (e.g., Khawaja et al., Reference Khawaja, Yang and Cockshaw2016; Zhang & Cheng, Reference Zhang and Cheng2022), or just one single overall effect size for a block of predictors (e.g., Sun et al., Reference Sun, Steinkrauss, Tendeiro and De Bot2016; Zhang et al., Reference Zhang, Dai and Liu2021). Thirdly, our study represents the first systematic attempt to consider English proficiency alongside Putonghua proficiency as correlates of well-being based on a big-data survey. Our endeavour to address the big question ‘is language linked to well-being’ and broaden the research scope on well-being turns out to be worthwhile: both the upper and lower limits of the effect size for English proficiency were higher than their counterparts for Putonghua proficiency, suggesting that English proficiency was a more important predictor for well-being than Putonghua proficiency. Put differently, it was problematic for the prototype paper (viz. Zhang & Cheng, Reference Zhang and Cheng2022) to overlook English proficiency and focus on Putonghua proficiency. Accordingly, we suggest that future studies pursuing the above big question adopt a more holistic perspective towards language, which includes the national language and FL.
Conclusion
Partly motivated by the seldom-explored linkage between one's FL proficiency and well-being (Zhang et al., Reference Zhang, Dai and Liu2021), the present study, based on the empirical data from a nationally representative big-data survey, has found that higher bilingualism is linked to a higher level of well-being, among others. We argue that to address the big question ‘is language linked to well-being’, ‘language’ should be inclusive enough rather than confined to the national language. Based on the finding, we hypothesise that bilingualism (operationalised as FL proficiency) is linked to well-being at a strength level (at least) comparable to the link between proficiency in the national language and well-being. This hypothesis will need to be further tested, modified, or falsified in further studies; before sufficient empirical data are accumulated in this regard, the time may not yet be ripe for the development of a theory focussing on the extent of the bilingualism-well-being linkage, which integrates bilingualism and other sociobiographical correlates (e.g., SES). Accordingly, we call for further replication studies to generate more empirical data, preferably based on representative samples such as the big-data sample used in our study. With empirical evidence from (partial) replications, it is then feasible to generate sufficient theoretical insight which helps pave the way for developing a theory; just as Bollier and Firestone (Reference Bollier and Firestone2010, p.8) rightfully point out, the more data there are, the better chances of ‘finding the “generators” for a new theory’.
In terms of methodological contributions, two major points merit attention. Firstly, thanks to the benefitsFootnote 16 of using big data, the present study represents one of the first attempts (e.g., Luo & Wei, Reference Luo and Wei2021; Wei et al., Reference Wei, Reynolds, Kong and Liu2022a) to utilise publicly available big-data surveys which are usually not designed for language-focused research purposes. We urge colleagues to explore and mine relevant data from those surveys to address issues of interest in the field of applied linguistics generally and in bilingualism research in particular. Secondly, we have made fuller use of effect sizes (in terms of both reporting and interpreting), compared with most previous studies. On the one hand, regarding effect size reporting, we provided not only a range of effect sizes via a more refined version of hierarchical regression, which could be usefully supplemented with dominance analysis, but also two or more types of effect sizes to facilitate cross-study comparisons. On the other hand, regarding effect size interpreting, we adopted an interpretation system that is more appropriate for survey-based research (Walton, Reference Walton2022; Wei & Hu, Reference Wei and Hu2019), rather than systems (e.g., Plonsky & Oswald, Reference Plonsky and Oswald2014) that are more relevant to experiment-based studies and/or have inflated benchmarks; one consequence of using those benchmark systems is that the results may have ‘discouraged researchers from delving further’ (Dewaele, Reference Dewaele, Sarch, Stephen and Marion2012, p. 43) into links of interest, such as the link between bilingualism and well-being in the present study. Responding to recent calls for stronger methodological rigour (Li, Reference Li2022; Wei & Hu, Reference Wei and Hu2021), we suggest that colleagues make fuller use of effect sizes in both reporting and interpreting the results.
Despite its substantive and methodological contributions, this study has three major limitations. First, despite the many advantages (e.g., sample representativeness) of using extant big-data surveys (e.g., the CGSS), one apparent disadvantage is that such surveys are not designed to satisfy all of the intended purposes of a particular research (Luo & Wei, Reference Luo and Wei2021). In future, studies similar to the present one will benefit from leveraging both big data and small data (e.g., experimental evidence generated from a small sample) (Wei et al., Reference Wei, Reynolds, Kong and Liu2022a). Second, besides the measure of bilingualism in our study, there are other useful measures, including Dewaele and Li's (Reference Dewaele and Li2013) operationalisation of bilingualism (or what they call ‘a global measure of multilingualism’). A different measure of bilingualism may generate a different set of results and interpretations. Third, although our study utilising a big data survey involved people from different occupations, it aimed to paint a general picture and hence did not probe into particular occupation groups. Given the recent calls for more research attention to non-student populations (e.g., Mercer, Reference Mercer2021; Wei & Su, Reference Wei and Su2015; Wei et al., Reference Wei, Wang and Li2022b), it will be useful for future studies based on non-big-data samples to focus on a particular occupation group such as business professionals and teachers (see e.g., Alqarni, Reference Alqarni2021). These issues merit continued research effort.
Supplementary Material
The supplementary material for this article can be found at https://doi.org/10.1017/S1366728923000603
Data availability
The data that support the findings of this study are openly available from https://osf.io/zd9k5/.
Acknowledgements
The authors would like to extend their thanks to the anonymous reviewers and the handling editor for their constructive comments on an earlier version of this paper. All remaining inadequacies are the authors’ responsibility. The writing of this paper was supported by the Bilingual Cognition and Development Lab, Center for Linguistics and Applied Linguistics, Guangdong University of Foreign Studies (No. BCD202009), the Chinese Society for Multilingualism and Multilingual Education affiliated to the International Association of Multilingual Education, and Xi'an Jiaotong-Liverpool University [grant number REF-19-02-01]. We also wish to thank our research assistants, Mr Hongzhong Chen, Mr Yuansheng Li, and Ms Yangxu Shen for their technical support (e.g., formatting).
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