Predicting adolescent truancy: The importance of distinguishing between different aspects of instructional quality |
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| Authors: | Christine Sälzer Ulrich Trautwein Oliver Lüdtke Margrit Stamm |
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| Institution: | 1. Institute of Education, University of Tuebingen, Europastrasse 6, 72072 Tuebingen, Germany;2. Department of Education, University of Fribourg, Rue Faucigny 2, 1700 Fribourg, Switzerland;1. Information Security Group, Smart Card Centre, Royal Holloway, University of London, Egham, United Kingdom;2. XLIM (UMR CNRS 7252/Université de Limoges) Département Mathématiques Informatique, Limoges, France;1. Hector Research Institute of Education Sciences and Psychology, University of Tuebingen, Europastraße 11, 72072 Tuebingen, Germany;2. Leibniz-Institute for Science and Mathematics Education at Kiel University, Olshausenstraße 62, 24118 Kiel, Germany;3. Goethe University Frankfurt, Theodor-W.-Adorno-Platz 6, 60629 Frankfurt am Main, Germany;1. Australian Catholic University, Australia;2. King Saud University, Saudi Arabia;3. University of Oxford, UK;4. Humboldt-University, Berlin, Germany;5. Hector Research Institute for Education Sciences and Psychology, University of Tübingen, Germany;6. Leibniz Institute for Science and Mathematics Education, Kiel, Germany;1. ASEAN Institute for Health Development, Mahidol University, Salaya, Phutthamonthon, Nakhon Pathom, Thailand;2. Department of Research Administration and Development, University of Limpopo, Turfloop, South Africa;3. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;4. Faculty of Pharmacy, Ton Duc Thang University, Ho Chi Minh City, Viet Nam |
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| Abstract: | Little is known about the association of classroom characteristics with adolescent truancy. A critical question is whether high achievement standards, high workload, and fast pace protect against or increase adolescent truancy. In this study, self-reports from 3491 Swiss grade 7, grade 8 and grade 9 students in 202 classes were used to predict truancy. Multilevel modeling was used to differentiate between the student and the class levels. High achievement standards were associated with a lower truancy rate at both the student and the class level, whereas fast instructional pace was associated with more truancy at both levels. A perception of the workload as being too low was an additional predictor of high truancy at both the student and the class level. |
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