Conventional multilevel modeling works well with purely hierarchical data; however, pure hierarchies rarely exist in real datasets. Applied researchers employ ad hoc procedures to create purely hierarchical data. For example, applied educational researchers either delete mobile participants' data from the analysis or identify the student only with…

The purpose of this paper is to showcase new research that seeks to provide guidance on the heterogeneity of treatment effects by utilizing the variance of demographic differences in state assessments. This study is focused on a simple randomized block design where students are nested within schools, and within each school students are randomized…

As applications of multilevel modelling in educational research increase, researchers realize that multilevel data collected in many educational settings are often not purely nested. The most common multilevel non-nested data structure is one that involves student mobility in longitudinal studies. This article provides a methodological review of…

Closing the achievement gap in public education is a worthy goal that has been included as a top priority in the No Child Left Behind Act of 2001 (2002). This study analyzed the most salient predictors at the student and school levels to identify their long-term impact on mathematics achievement from the elementary grades to high school. The…