Assumptions of value‐added models for estimating school effects

The ability of school (or teacher) value‐added models to provide unbiased estimates of school (or teacher) effects rests on a set of assumptions. In this paper, we identify six assumptions that are required in order that the estimands of such models are well‐defined and that the models are able to recover the desired parameters from observable data. These assumptions are 1) manipulability; 2) no interference between units; 3) interval scale metric; 4) homogeneity of effects; 5) strongly ignorable assignment; and 6) functional form.

A conceptual framework for measuring segregation and its association with population outcomes

"Social epidemiology is a comparatively new field of inquiry that seeks to describe and explain the social and geographic distribution of health and of the determinants of health. This book considers the major methodological challenges facing this important field. Its chapters, written by experts in a variety of disciplines, are most often authoritative, typically provocative, and often debatable, but always worth reading."
—Stephen W. Raudenbush, Lewis-Sebring Distinguished Service Professor, Department of Sociology, University of Chicago