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- W2620709097 abstract "Introduction One important consideration when designing large-scale experiments is to ensure that the design is sensitive enough to detect the expected intervention effect. This task involves making decisions about sample sizes to ensure sufficient statistical power of the test of the treatment effect. Statistical power is defined as the probability of rejecting the null hypothesis when it is false. Extensive literature exists on the computation of statistical power (e.g., Cohen, 1988; Kraemer & Thiemann, 1987; Lipsey, 1990; Murphy & Myors, 2004). Much of the literature, however, involves the computation of power in studies that use simple random samples; hence nesting effects are typically not included in power computations. In education and the social sciences, populations of interest have often multilevel structures, for example, students are nested within classrooms or schools. Because individuals within aggregate units are often more alike than individuals in different units," @default.
- W2620709097 created "2017-06-09" @default.
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- W2620709097 date "2013-05-01" @default.
- W2620709097 modified "2023-09-23" @default.
- W2620709097 title "Constructing a More Powerful Test in Two-Level Block Randomized Designs" @default.
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- W2620709097 doi "https://doi.org/10.22237/jmasm/1367381220" @default.
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