Dropout in secondary education: an application of a multilevel discrete-time hazard model accounting for school changes

Abstract

For several decades, researchers have focused on dropout in search for an explanation and prevention of this phenomenon. However, past research is characterized by methodological shortcomings. Most of this research was conducted without considering the hierarchical structure of educational data and ignored the longitudinal path towards dropout. Moreover, research that did take into account these shortcomings, did not correct for student mobility between schools, despite the strong correlation with dropout (South et al. 2007). In this study, we attempt to address these shortcoming by implementing a multilevel discrete-time hazard model and exploring the effect of different school classifications on the school effects. Partially analogous to Grady and Beretvas (2010) we compare models with estimated school effects based on the first and on the last school attended and compare these models with multiple membership models and cross-classified models. The results of this comparison indicate that ignoring student mobility can have strong implications on the predictors of dropout. Not only do models which take into account this mobility yield better model fits, models ignoring this mobility tend to miss the effect of school level variables. With respect to the conclusions on dropout research, our models provide evidence for the often cited student characteristics predicting dropout and indicate stronger school effects than generally assumed.

The Impact of Institutional Context, Education and Labour Market Policies on Early School Leaving: a comparative analysis ofEUcountries

“…First, there is an extensive literature correlating characteristics of students to early school leaving (ESL). For instance, it has been argued that boys, students retained in grade, students with a low socio‐economic status or from ethnic minorities, and little‐engaged and low‐performing students are more likely to leave school before obtaining a qualification (for an overview see Alexander, Entwisle, & Kabbani, ; Lamb, 2011b; Lamote, Van Damme, Van Den Noortgate, Speybroeck, Boonen, & de Bilde, ; Nevala & Hawley, ; Rumberger, ; De Witte et al ., ). This article differs from the earlier literature by focusing on country characteristics rather than on student‐specific elements.…”

The Long-Term Impact of Systemic Student Support in Elementary School: Reducing High School Dropout

“…Previous studies have examined dropout employing discrete time hazards models (Bowers, 2010; Lamote et al, 2013). This analytic approach extends earlier work by using a large sample in which an intervention was implemented in a “real world” setting, applying propensity score weights, and taking into account school attendance, as recommended by standards.…”

Winding Paths through School and After: Young Swedes of Migrant Origin Who Failed in Upper Secondary School

“…A factor that has received less attention until recently is that school itself may contribute to failure (de Witte & Rogge, 2013). Inability to detect early warnings, insufficient provision of individual support, neglect of bullying, and in some cases even discrimination against or harassment of students by staff are school-related factors that have been shown to contribute to truancy and poor academic results (Cornell et al, 2013;Elffers, 2012;Lamote et al, 2013). Higher proportions of students of migrant and ethnic minority backgrounds leave school without complete grades than other students, but several studies have shown that socio-economic conditions strongly contribute to such differences (Elffers, 2012;Lundahl & Lindblad, 2018).…”