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Erscheinungsjahr: 
2023
Quellenangabe: 
[Journal:] Zeitschrift für Erziehungswissenschaft [ISSN:] 1862-5215 [Volume:] 26 [Issue:] 2 [Publisher:] Springer Nature [Place:] Berlin [Year:] 2023 [Pages:] 469-494
Verlag: 
Springer Nature, Berlin
Zusammenfassung: 
The dropout rate in vocational education and training (VET) programs is high in Germany. Yet, this study shows that most instances of dropout are not permanent departures from VET. Instead, most trainees later re-enter into a new training program in a different occupation or company (a phenomenon labelled “stopout”). We use Tinto’s model of college dropout, which suggests that the importance of performance-related and integration-related risk factors differs for permanent dropouts and stopouts, and their timing. Our analyses are based on longitudinal data from the National Educational Panel Study (NEPS), which provides measures for competencies and socioemotional (also called noncognitive) skills prior to starting VET programs, information to capture factors related to dis/satisfaction with training and longitudinal information on school-to-work transitions. To examine the relative importance of performance- and integration-related factors, we use Shapley decompositions. Our analyses reveal that performance-related risk factors (in terms of low math competence) increase the risk of permanently dropping out, and both cognitive and socioemotional skills predict occupational changes (occupational stopouts). Whether the VET program corresponds to the desired occupation is the most important integration-related factor, and a poor match increases the risk of both permanent dropout and occupational stopout. Moreover, lower satisfaction with training leads to changes in occupations or training companies. Finally, early training dissolutions are more strongly linked to integration-related problems, while later ones are more due to performance-related problems.
Schlagwörter: 
Dropout
Stopout
VET
Competences
Noncognitive skills
Socioemotional skills
Prevocational training
Shapley decomposition
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