Exploring different types of school refusers through latent profile analysis and school-related stress associations

Abstract

Children experience significant number of stressful situations at school during their academic years. The aims of this study were to identify school refusers groups of children through latent profile analysis and to test their associations with school-related sources and manifestation of stress. Data were obtained from 755 schoolchildren (8–11 years) from public and private schools of Alicante and Murcia (Spain), using the School Refusal Assessment Scale-Revised (SRAS-R) and the School Situation Survey (SSS). In general, positive and statistically significant correlations were identified between school refusal behavior and school-related sources and manifestations of stress. School refusers groups were derived from the combination of high and low scores in the four functional conditions assessed by the SRAS-R. “Low School Refusal Behavior Profile,”“School Refusal Behavior by Positive Reinforcement Profile” and “Mixed School Refusal Behavior Profile” were identified. The percentages of children within these profiles were 47.7%, 46.6%, and 5.7%, respectively. The Mixed School Refusal Behavior Profile was the group with the highest average scores in the school-related stress factors. In contrast, the group with the lowest mean scores was the Low School Refusal Behavior Profile. By comparing the Low School Refusal Behavior Profile with the Mixed School Refusal Behavior Profile, the largest effect sizes were found. Findings are discussed from a socio-ecological perspective considering the school context conditions as key elements in the development of school refusal behaviors.

Measurement Invariance and Latent Profile Analysis of the Test Anxiety Inventory

“…These indices include the lowest values of the Bayesian Information Criterion (BIC) and the Akaike Information Criteria (AIC), entropy values closer to 1, and the Bootstrap Likelihood Ratio Test (BLRT) p-values smaller than 0.05 (Bauer, 2021;Li et al, 2022). More so, any class should contain at least a distribution of 25 participants or more (Gonzálvez et al, 2021), and the interpretability of the models (Bauer, 2021) should be taken into consideration in the analysis. (Finney & DiStefano, 2006).…”