| Abstract: | This article discusses issues associated with statistical testing conducted with data from clustered school samples. Empirical researchers often conduct tests of statistical inference on sample data to ascertain the extent to which differences exist within groups in the population. Typically, much school‐related data are collected from students. These data are hierarchical because students are nested within classes within schools. This article studies the influence of this nesting on tests of statistical significance conducted with the student as the unit of analysis. Theory that adjusts F‐test scores for nested data in multi‐group comparisons is presented and applied to a teacher interaction dataset. The article demonstrates the potential impact of data hierarchy on the results of statistical testing if clustering is ignored. Data analysis techniques that recognize the clustering of students in classes are essential, and it is recommended that either multilevel analysis or adjustments to statistical parameters be undertaken in studies involving nested data. |
