The Big-fish-little-Pond effect is well acknowledged as the negative effect of class/school average achievement on student academic self-concept, which profoundly impacts student academic performance and mental development. Although a few studies have been done with regard to this effect, inconsistence exists in the effect size with little success in finding moderators. Here, we present a meta-analysis to synthesize related literatures to reach a summary conclusion on the BFLPE. Furthermore, student age, comparison target, academic self-concept domain, student location, sample size, and publication year were examined as potential moderators. Thirty-three studies with fifty-six effect sizes (total N = 1,276,838) were finally included. The random effects model led to a mean of the BFLPE at β = −0.28 (p < 0.001). Moreover, moderator analyses revealed that the Big-Fish-Little-Pond effect is an age-based process and an intercultural phenomenon, which is stronger among high school students, in Asia and when verbal self-concept is considered. This meta-analysis is the first quantitative systematic overview of BFLPE, whose results are valuable to the understanding of BFLPE and reveal the necessity for educators from all countries to learn about operative means to help students avoid the potential negative effect. Future research expectations are offered subsequently.

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Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004), Vermunt’s (2010) maximum likelihood (ML) approach, and the inclusive three-step approach of Bray, Lanza, & Tan (2015). These methods have been studied in the related case of latent class analysis (LCA) with categorical indicators, but not as well studied for LPA with continuous indicators. We investigated the performance of these approaches in LPA with normally distributed indicators, under different conditions of distal outcome distribution, class measurement quality, relative latent class size, and strength of association between latent class and the distal outcome. The modified BCH implemented in Latent GOLD had excellent performance. The maximum likelihood and inclusive approaches were not robust to violations of distributional assumptions. These findings broadly agree with and extend the results presented by Bakk and Vermunt (2016) in the context of LCA with categorical indicators.

Few studies examining the link between personality and alcohol use have adopted a comprehensive modeling framework to take into account individuals’ profiles across multiple personality traits. In this study, latent profile analysis (LPA) was applied to a national sample of young adults in the United States to identify subgroups defined by their profiles of mean scores on the Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness personality factors. Personality profiles were then used to predict heavy drinking. Five profiles were identified: Reserved, Rigid, Confident, Ordinary, and Resilient. Compared to individuals in the Ordinary profile, those with Reserved and Resilient profiles were at increased risk of frequent heavy drinking. These findings suggest which comprehensive personality profiles may place individuals at risk for problematic alcohol-related outcomes.

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