School non-attendance and learned helplessness: latent profiles and ROC curves

TL;DR

This study explores how learned helplessness relates to school non-attendance, identifying distinct profiles and showing how helplessness can predict severe absenteeism.

Contribution

The study introduces latent profiles of school absenteeism and demonstrates the predictive power of learned helplessness for high non-attendance.

Findings

Three distinct profiles of school absenteeism were identified using the ARSNA.

Learned helplessness significantly differentiates between absenteeism profiles.

LH shows strong predictive and discriminative ability for high school non-attendance.

Abstract

Due to the complex school reality, Learned Helplessness (LH) is a student’s response characterized by lack of confident, interpretative bias and negative outlook of success in face of school challenges. These helpless students develop a negative attitude toward school, leading to a withdrawal of school engagement and anxious disorders, reporting links with emotionally based school non-attendance. Taking into account the heterogeneous causes of these problems, in recent years new instruments have been emerged, like Assessing Reasons for School Non-Attendance (ARSNA; Havik et al., 2015), of which there is a clear lack of research. The study aims to: (1) identify latent profiles of school absenteeism based on Assessing Students’ Reported Reasons for School Non-attendance (ARSNA; Havik et al., 2015); (2) analyze differences between school non-attendance profiles and Learned Helplessness (LH); and (3) establish the predictive and discriminative capacity of LH to identify students of the high school non-attendance profile. Consequently, 759 adolescents (M = 14.95, SD = 1.82) fulfilled ARSNA (Havik et al., 2015) and Learned Helplessness Questionnaire (LHQ; Sorrenti et al., 2015). Pearson’s correlation coefficients reported positive and statistically significant correlations between ARSNA dimensions and LH. Latent Profile Analyses revealed 3 school absenteeism profiles. ANOVA indicated statistically significant differences between these profiles and LH. Finally, Logistic Regression and ROC Curves found the predictive and discriminative ability of LH to identify individuals of the high school non-attendance profile. Results contribute to the literature on ARSNA dimensions and LH, highlighting the potential implications for schools and for the intervention against emotionally based school non-attendance.

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