Journal of Intelligence

Although numerous studies address inclusive education, especially in Latin America, research analyzing the overall life satisfaction of teachers in schools that implement inclusion policies are scarce. The purpose of this study is to analyze the relationship between Life Satisfaction, Optimism, Culture, and the Inclusive Practice of primary school teachers from Chile. A descriptive quantitative method was employed, with an ex post facto design including 246 primary teachers from urban and rural schools in Chile. The teachers completed four questionnaires: Inclusive Culture (IC), Inclusive Practice (IP) Satisfaction with Life Scale (SWSL), and Life Orientation Test Revised (LOT-R). The results show that these instruments present acceptable reliability. In addition, a significant correlation was found between Classroom Experience Time (CET) and SWSL (r = 0.201, p < .01), as well as between SWSL, and LOT-R (r = 0.411, p < .01), and IC and IP (r = 0.838, p < .01). The regression model is statistically significant [F (4, 241) = 139.572, p < .001]. The findings indicate that IC and SWSL predict IP directly, whereas CET is an inverse predictor. There is a statistically significant relationship between Life Satisfaction, Classroom Experience Time, Culture, and Inclusive Practice, with the three first variables being predictors of Inclusive Practice.

Complex motor tasks that integrate cognitive demands may particularly enhance executive functions, which support school success. Yet few school-based trials have tested structured interventions combining motor complexity and cognitive challenge in early adolescence. Purpose: This study examined the effects of a gamified “Dual-Challenge Circuit” (DCC), integrating motor patterns with cognitive tasks, on executive functions, academic performance, motor skills, and physical fitness among middle school students. Secondary aims were to explore whether executive functions mediated academic gains and whether a dose–response relationship emerged. Method: A cluster-randomized controlled trial was conducted in four middle schools in Southern Italy with sixth- and seventh-grade students. Participants were assigned to either the DCC program or traditional physical education. The 12-week intervention included two weekly 60 min sessions. Outcomes were executive functions (Stroop, Digit Span backward, Trail Making Test-B), academic achievement (grades, MT tests), motor coordination (KTK), physical fitness (PACER, long jump, sit-and-reach), and adherence/fidelity. Results: The DCC group showed significantly greater improvements in all executive function measures and in mathematics and language grades (medium-to-large effects). Mediation analyses confirmed executive functions predicted academic improvements. Motor coordination and fitness also improved, with large effects in aerobic capacity and strength. Conclusions: The DCC effectively enhanced executive functions, academic outcomes, and fitness. Gamified, cognitively demanding physical education formats appear feasible and beneficial in real-world school settings.

In psychology, small sample sizes are a frequent challenge—particularly when studying specific expert populations or using complex and cost-intensive methods like human scoring of creative answers—as they reduce statistical power, bias results, and limit generalizability. They also hinder the use of frequentist confirmatory factor analysis (CFA), which depends on larger samples for reliable estimation. Problems such as non-convergence, inadmissible parameters, and poor model fit are more likely. In contrast, Bayesian methods offer a robust alternative, being less sensitive to sample size and allowing the integration of prior knowledge through parameter priors. In the present study, we introduce small-sample-size structural equation modeling to creativity research by investigating the relationship between creative fluency and nested creative cleverness with right-wing authoritarianism, starting with a sample size of N = 198. We compare the stability of results in frequentist and Bayesian SEM while gradually reducing the sample by n = 25. We find that common frequentist fit indexes degrade below N = 100, while Bayesian multivariate Rhat values indicate stable convergence down to N = 50. Standard errors for fluency loadings inflate 40–50% faster in frequentist SEM compared to Bayesian estimation, and regression coefficients linking RWA to cleverness remain significant across all reductions. Based on these findings, we discuss (1) the critical role of Bayesian priors in stabilizing small-sample SEM, (2) the robustness of the RWA-cleverness relationship despite sample constraints, and (3) practical guidelines for minimum sample sizes in bifactor modeling.