A Hierarchical Model for Studying School Effects

TLDR

Research on how school policies influence student outcomes relies on multilevel hierarchical data, yet traditional statistical methods struggle with their inferential challenges and no comprehensive alternative has existed. This paper introduces a general statistical methodology for analyzing hierarchically structured school data and demonstrates its application to the High School and Beyond study and the debate over public versus Catholic school effectiveness. The model treats mean achievement and equity-related structural parameters as multivariate school‑level outcomes, whose variation is modeled as a function of school characteristics.

Abstract

When researchers investigate how school policies, practices, or climates affect student outcomes, they use multilevel, hierarchical data. Though methodologists have consistently warned of the formidable inferential problems such data pose for traditional statistical methods, no comprehensive alternative analytic strategy has been available. This paper presents a general statistical methodology for such hierarchically structured data and illustrates its use by reexamining the High School and Beyond data and the controversy over the effectiveness of public and Catholic schools. The model enables the researcher to utilize mean achievement and certain structural parameters that characterize the equity in the social distribution of achievement as multivariate outcomes for each school. Variation in these school-level outcomes is then explained as a function of school characteristics.