Identifying the top predictors of student well-being across cultures using machine learning and conventional statistics.

Alongside academic learning, there is increasing recognition that educational systems must also cater to students' well-being. This study examines the key factors that predict adolescent students' subjective well-being, indexed by life satisfaction, positive affect, and negative affect. Data from 522,836 secondary school students from 71 countries/regions across eight different cultural contexts were analyzed. Underpinned by Bronfenbrenner's bioecological theory, both machine learning (i.e., light gradient-boosting machine) and conventional statistics (i.e., hierarchical linear modeling) were used to examine the roles of person, process, and context factors. Among the multiple predictors examined, school belonging and sense of meaning emerged as the common predictors of the various well-being dimensions. Different well-being dimensions also had distinct predictors. Life satisfaction was best predicted by a sense of meaning, school belonging, parental support, fear of failure, and GDP per capita. Positive affect was most strongly predicted by resilience, sense of meaning, school belonging, parental support, and GDP per capita. Negative affect was most strongly predicted by fear of failure, gender, being bullied, school belonging, and sense of meaning. There was a remarkable level of cross-cultural similarity in terms of the top predictors of well-being across the globe. Theoretical and practical implications are discussed.

Socio-economic status, mastery-approach goals and learning-related outcomes: Mediation and moderation.

BACKGROUND: Socio-economic status is one of the most important factors shaping students' motivation and achievement but has seldom been explored in relation to achievement goals. AIMS: This study aimed to investigate whether mastery-approach goals explain the link between SES and key learning-related outcomes (mediation) and whether SES modifies the relationship between mastery-approach goals and these outcomes (moderation). SAMPLE: Data came from 595,444 students nested in 21,322 schools across 77 countries. METHODS: Data were analysed using multilevel-moderated mediation analyses. RESULTS: We found significant mediation and moderation. In terms of mediation, mastery-approach goals mediated the association between family SES and learning-related outcomes. However, a different pattern emerged for school SES, as students in higher SES schools had lower mastery-approach goals. In terms of moderation, we found that family SES strengthened the association between mastery-approach goals and learning-related outcomes. However, the association between mastery-approach goals and learning-related outcomes was weaker in higher SES schools. CONCLUSION: Theoretical and practical implications for the achievement goal approach to achievement motivation are discussed.

Bifactor Structure for the Categorical Chinese Rosenberg Self-Esteem Scale.

Recently, the bifactor model was suggested for the latent structure of the Rosenberg Self-Esteem Scale (RSES). The present paper investigates (i) the differences among bifactor, bifactor negative and other models; (ii) the effects of treating data as both categorical vs continuous; (iii) whether a problematic item in the Chinese RSES should be removed; and (iv) whether the final scoring would be affected. With a sample of 1.734 grade 4-6 school pupils in Hong Kong, we used BIC differences in addition to the usual model fit indices, and found that there was strong evidence for using the bifactor model (RMSEA = .052, 90% CI [.043, .062], CFI = .992, TLI = .984 for 9-item RSES categorical). Little difference is found between treating data as categorical or continuous for fit indices, but the factor loading patterns are better in categorical case. Keeping a problematic item has little effect on fit indices, but would lead to unexpected negative loading. The ranking of loadings within positive and negative items across different conditions are the same, which has important effects on scoring. Loadings in the method effects in the bifactor models are all positive (p < .001), which is different from previous research. All models show similar results on scoring, and support the usual simple sum score in most practice.

Application of latent variable model in Rosenberg self-esteem scale.

Latent Variable Models (LVM) are applied to Rosenberg Self-Esteem Scale (RSES). Parameter estimations automatically give negative signs hence no recoding is necessary for negatively scored items. Bad items can be located through parameter estimate, item characteristic curves and other measures. Two factors are extracted with one on self-esteem and the other on the degree to take moderate views, with the later not often being covered in previous studies. A goodness-of-fit measure based on two-way margins is used but more works are needed. Results show that scaling provided by models with more formal statistical ground correlated highly with conventional method, which may provide justification for usual practice.

Perceived level of knowledge and difficulty in applying family assessment among senior undergraduate nursing students.

Because the structure, development, and functioning of a family plays an important role in health and illness, preparing nursing students to assess families in health care settings is of critical importance. A quasi-experimental design using a pre- and postcourse questionnaire was used to examine students' perceived knowledge about family assessment and perceived difficulty applying family assessment in the clinical setting. The Calgary Family Assessment Model (CFAM) was taught in an elective nursing course, "Families in Health and Illness," offered at the University of Hong Kong. At the completion of the course, 46 senior baccalaureate nursing students showed a significant increase in their perceived understanding of all subcategories in CFAM compared with the control group of 43 senior baccalaureate nursing students who completed an elective nursing course in women's health. Teaching family nursing assessment in undergraduate programs may be useful in ensuring that nurses attend to families in practice.

The acquisition of Cantonese classifiers by preschool children in Hong Kong.

The Cantonese language has a complex classifier system and young learners need to pay attention to both the semantics and syntax of classifiers. This study investigated the repertoire of classifiers produced by 492 Cantonese-speaking preschoolers in three age groups (3;0, 4;0 and 5;0). Spontaneous utterances produced in 30-minute toy-play contexts were collected and transcribed. Analyses identified a productive repertoire of 73 classifiers in the utterances, which could be appropriately classified into the typology proposed in the present study. An age-related increase in the number of classifier types per child as well as the repertoire size of each group was found. [symbol: see text] go3 (CL) was widely used as the general classifier by the young children. It was also discovered that the three-year-olds were already showing signs of grasping the basic syntax of classifiers. Cognitive, linguistic and contextual influences presumed to shape the evidence are discussed.

A goodness of fit test for sparse 2p contingency tables.

When a model is fitted to data in a 2p contingency table many cells are likely to have very small expected frequencies. This sparseness invalidates the usual approximation to the distribution of the chi-squared or log-likelihood tests of goodness of fit. We present a solution to this problem by proposing a test based on a comparison of the observed and expected frequencies of the second-order margins of the table. A chi2 approximation to the sampling distribution is provided using asymptotic moments. This can be straightforwardly calculated from the expected cell frequencies. The new test is applied to several previously published examples relating to the fitting of latent variable models, but its application is quite general.