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FINNUT-Forskning og innovasjon i utdanningssektoren

Transgenerational, Social, and Individual Predictors of High-School Dropout: An Ecological Model Tested in a Multi-Data Design

Alternative title: Distale og proksimale faktorer for frafall i videregående skole - en holistisk modell undersøkt gjennom flerstudieanalyser.

Awarded: NOK 12.3 mill.

More than 25% of Norwegian high-school students (16-19 years) fail to graduate within five years after admission (n>10 000). Youth dropping out from school are at risk for a multitude of problems, and the current COVID-19 pandemic is likely to excel the number of individuals vulnerable to social marginalization. High-school dropout is often just an endpoint of a process that for many starts early in life. Hence, policies and interventions preventing dropout trajectories at an early stage bear promise of greater success than remediating efforts. In DROPOUT, we identify the impact of generational, social, and individual factors, as well as map the complex interplay of such factors from childhood to adolescence by employing a multi-data design and innovative statistical methods. We take advantage of prospective multi-informant data with up to 8 measure points (The Trondheim Early Secure Study) together with data spanning over several generations with rich possibilities for links to register data (The Trøndelag Health Study). Analyses have been instigated, in collaboration with national and international partners, and the first results are likely to be published in spring 2022.

More than 25% of Norwegian high-school students (16-19 years) fail to graduate within five years after admission (n>10 000). Youth dropping out from school are at risk for a multitude of problems, and the current COVID-19 pandemic is likely to excel the number of individuals vulnerable to social marginalization. High-school dropout is often just an endpoint of a process that for many starts early in life. Hence, policies and interventions preventing dropout trajectories at an early stage bear promise of greater success than remediating efforts. In DROPOUT, we propose to identify the impact of generational, social, and individual factors, as well as map the complex interplay of such factors from childhood to adolescence by employing a multi-data design and innovative statistical methods. Even prospective studies are at risk for confusing correlate with cause. Hence, recent statistical approaches, focusing on within-person analyses, reduce this risk of false conclusions, by discounting all unmeasured time-invariant confounders—including genetics. Moreover, within-person analyses may be expanded to also adjust for measured time-varying confounding, thereby further reducing the risk of mischaracterizing a correlate as a causal factor. We will take advantage of prospective multi-informant data with up to 8 measure points (The Trondheim Early Secure Study) together with data spanning over several generations with rich possibilities for links to register data (The Trøndelag Health Study). These comprehensive data pools, containing different assets with respect to variable complexity, measure point proximities, and representativeness/statistical power, combined with the team's high statistical competence, put our research team in position to provide important insights into the short-term and the long-term causal relationships leading to increased/decreased likelihood for high-school dropout.

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FINNUT-Forskning og innovasjon i utdanningssektoren