Many students drop out in the first year after a school transition. Most commonly used indicators of an increased risk for dropout reveal little of the mechanisms that push or pull students out of school. In this study, we look at the association of a set of common risk indicators with students' supportive resources and school experiences upon the transition to post-secondary vocational education in the Netherlands. Multilevel regression analyses on a diverse sample of 1438 students indicate that most sociodemographic risk indicators relate to less access to supportive resources for school, whereas personal circumstances outside school that are associated with an increased risk for dropout correlate with negative school experiences. Students from lower educated or poor families and students who use drugs, have debts, or are delinquent score negative in both domains, suggesting that those students make the transition with one foot out the school door.
Conducting a power analysis can be challenging for researchers who plan to analyze their data using structural equation models (SEMs), particularly when Monte Carlo methods are used to obtain power. In this tutorial, we explain how power calculations without Monte Carlo methods for the χ2 test and the RMSEA tests of (not-)close fit can be conducted using the Shiny app “power4SEM”. power4SEM facilitates power calculations for SEM using two methods that are not computationally intensive and that focus on model fit instead of the statistical significance of (functions of) parameters. These are the method proposed by Satorra and Saris (Psychometrika 50(1), 83–90, 1985) for power calculations of the likelihood ratio test, and that described by MacCallum, Browne, and Sugawara (Psychol Methods 1(2) 130–149, 1996) for RMSEA-based power calculations. We illustrate the use of power4SEM with examples of power analyses for path models, factor models, and a latent growth model.
Behavioral disengagement from school is a proximal predictor of dropout. Therefore, the enhancement of behavioral engagement is a useful point of entry for dropout prevention. In this study, we examine the behavioral engagement of at-risk and non-at-risk students in Dutch senior vocational education (SVE), a sector confronted with high dropout rates. Using multilevel regression analyses, we assess the role of students' background characteristics and perceived fit with the school environment in their behavioral engagement. Findings indicate that students in highly urbanized areas are significantly less engaged in school. The perceived proportion of autonomous work is most prominently correlated to students' behavioral engagement. Whereas in general SVE students are more engaged if their program requires little autonomous work from students, engineering students appear to favor autonomous work forms.
In this study, we examine students' educational attitudes upon the transition to Dutch senior vocational education (SVE), a transition associated with high dropout rates in the first year. Prior studies have identified differences in educational attitudes between sociodemographic groups. However, the mechanisms underlying those differences remain topic of debate: some studies point at differences in the school orientation and support in students' social communities outside school, others focus on differences in educational experiences between sociodemographic groups. Multilevel sequential regression analyses on a diverse sample of 1438 students in urban SVE schools reveal that students have very positive educational attitudes upon their transition to SVE. Ethnic minority students express particularly positive attitudes. School-related encouragement and support at home plays an important role in students' attitudes, but the attitudes of students from lower educated or ethnic minority communities are less related to this support. Prior school experiences play an essential, but occasionally counterproductive, role in students' attitudes upon transition, depicting the transition as a fresh new start for some, and an unwelcome threshold for others.
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