Chinese parents' school-readiness beliefs and parenting styles: patterns and associated factors.

Introduction: A smooth transition to primary school is positively related to children's later school experience. Certain parental school-readiness beliefs and parenting styles, among other factors, contribute to the smoothness of this transition. Methods: Therefore, this study adopted a latent profile analysis to examine the patterns of Chinese parents' school-readiness beliefs and their parenting styles and investigated socioeconomic status (SES) differentials in both. Two-stage probability sampling method is adopted in this study and a total of 1,204 Chinese parents of 5- to 6 years-old children were investigated with school-readiness beliefs scale, Parenting Styles and Dimensions Questionnaire, as well as scale of attitudes regarding roles in school readiness All data analyses were processed in Mplus 8.6. Results and discussion: Three profiles were identified: (1) supportive parenting with a very strong emphasis on school readiness; (2) partially supportive parenting with a reflection of school readiness; (3) weakly supportive parenting with no emphasis on school readiness. Higher SES was found to be more likely to be associated with membership in Profile 1 rather than Profile 2. The present study shows quantitative support for Anette Lareau's work and has implications for the development of more targeted parental intervention programs.

Neglect of publication bias compromises meta-analyses of educational research.

Because negative findings have less chance of getting published, available studies tend to be a biased sample. This leads to an inflation of effect size estimates to an unknown degree. To see how meta-analyses in education account for publication bias, we surveyed all meta-analyses published in the last five years in the Review of Educational Research and Educational Research Review. The results show that meta-analyses usually neglect publication bias adjustment. In the minority of meta-analyses adjusting for bias, mostly non-principled adjustment methods were used, and only rarely were the conclusions based on corrected estimates, rendering the adjustment inconsequential. It is argued that appropriate state-of-the-art adjustment (e.g., selection models) should be attempted by default, yet one needs to take into account the uncertainty inherent in any meta-analytic inference under bias. We conclude by providing practical recommendations on dealing with publication bias.