Gauging Causality in Multilevel Models

Abstract

This book illustrates strategies for the development and testing of multilevel models bearing on social problems, all of which deal directly or indirectly on aspects of human development, measured by social and economic indicators. It confronts social problems by ideally following these five steps: analyze the roots of the social problem both theoretically and empirically; formulate a study design that captures the nuances of the problem; gather empirical data bearing on the social problem that enable the design to be operationalized by forming identifiable and repeatable measures; model the multilevel data using appropriate multilevel statistical methods to uncover potential causes and any bias to their effects; and use the results to sharpen theory and to formulate evidence-based policy recommendations for implementation and testing. Applying this process, the core chapters present multilevel models focusing on political extremism, global human development, violence against minorities, the substantive complexity of work, reform of urban schools, and problems of health care. The reader will be better able to conduct state-of-the-art studies on these and other topics by gaining an understanding of these chapters and by using the available data sets and analytic programs to replicate and advance the analyses.