Between-School Variation in Students' Achievement,Motivation, Affect,and Learning Strategies: Results from 81 Countries for Planning Group-Randomized Trials in Education |
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Authors: | Martin Brunner Ulrich Keller Marina Wenger Antoine Fischbach Oliver Lüdtke |
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Institution: | 1. Faculty of Human Sciences, University of Potsdam, Potsdam, Germanymartin.brunner@uni-potsdam.de;3. University of Luxembourg, Luxembourg Centre for Educational Testing, Esch/Alzette, Luxembourg;4. Department of Education and Psychology, Freie Universit?t Berlin, Berlin, Germany;5. Leibniz Institute for Science and Mathematics Education, Centre for International Student Assessment, Kiel, Germany |
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Abstract: | To plan group-randomized trials where treatment conditions are assigned to schools, researchers need design parameters that provide information about between-school differences in outcomes as well as the amount of variance that can be explained by covariates at the student (L1) and school (L2) levels. Most previous research has offered these parameters for U.S. samples and for achievement as the outcome. This paper and the online supplementary materials provide design parameters for 81 countries in three broad outcome categories (achievement, affect and motivation, and learning strategies) for domain-general and domain-specific (mathematics, reading, and science) measures. Sociodemographic characteristics were used as covariates. Data from representative samples of 15-year-old students stemmed from five cycles of the Programme for International Student Assessment (PISA; total number of students/schools: 1,905,147/70,098). Between-school differences as well as the amount of variance explained at L1 and L2 varied widely across countries and educational outcomes, demonstrating the limited generalizability of design parameters across these dimensions. The use of the design parameters to plan group-randomized trials is illustrated. |
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Keywords: | student achievement motivation affect learning styles intraclass correlation large-scale assessment multilevel models design parameters |
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