Using Conditional Inference Trees to (Re)Explore Nonprofit Board Composition

Abstract

This Research Note introduces nonprofit scholars to the contemporary analytical tool of conditional inference trees as a means to shed more light on the institutional forces behind the changing composition of nonprofit boards of trustees. Revisiting the data of the Six-Cities Cultures of Trusteeship Project, this note illustrates the illuminating power of conditional inference trees for analyzing data (particularly categorical data), not well served by significance testing. Applying these popular models adds depth, nuance, and increased clarity to some of the original findings from the Six-Cities research project. This empirical case serves as a how-to for future researchers hoping to more flexibly model the relative impact of institutional (and other) variables on nonprofit organization structures, as well as expand their methodological toolkit when dealing with all sorts of regression problems.