@article{BrunnerKellerWengeretal.2017,
  author    = {Brunner, Martin and Keller, Ulrich and Wenger, Marina and Fischbach, Antoine and L{\"u}dtke, Oliver},
  title     = {Between-School Variation in Students' Achievement, Motivation, Affect, and Learning Strategies},
  series = {Journal of research on educational effectiveness / Society for Research on Educational Effectiveness (SREE)},
  volume    = {11},
  journal   = {Journal of research on educational effectiveness / Society for Research on Educational Effectiveness (SREE)},
  number    = {3},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1934-5747},
  doi       = {10.1080/19345747.2017.1375584},
  pages     = {452 -- 478},
  year      = {2017},
  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.},
  language  = {en}
}
@article{StallaschLuedtkeArteltetal.2021,
  author    = {Stallasch, Sophie E. and L{\"u}dtke, Oliver and Artelt, Cordula and Brunner, Martin},
  title     = {Multilevel design parameters to plan cluster-randomized intervention studies on student achievement in elementary and secondary school},
  series = {Journal of research on educational effectiveness},
  volume    = {14},
  journal   = {Journal of research on educational effectiveness},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1934-5747},
  doi       = {10.1080/19345747.2020.1823539},
  pages     = {172 -- 206},
  year      = {2021},
  abstract  = {To plan cluster-randomized trials with sufficient statistical power to detect intervention effects on student achievement, researchers need multilevel design parameters, including measures of between-classroom and between-school differences and the amounts of variance explained by covariates at the student, classroom, and school level. Previous research has mostly been conducted in the United States, focused on two-level designs, and limited to core achievement domains (i.e., mathematics, science, reading). Using representative data of students attending grades 1-12 from three German longitudinal large-scale assessments (3,963 <= N <= 14,640), we used three- and two-level latent (covariate) models to provide design parameters and corresponding standard errors for a broad array of domain-specific (e.g., mathematics, science, verbal skills) and domain-general (e.g., basic cognitive functions) achievement outcomes. Three covariate sets were applied comprising (a) pretest scores, (b) sociodemographic characteristics, and (c) their combination. Design parameters varied considerably as a function of the hierarchical level, achievement outcome, and grade level. Our findings demonstrate the need to strive for an optimal fit between design parameters and target research context. We illustrate the application of design parameters in power analyses.},
  language  = {en}
}