ABSTRACT
This chapter presents three quantitative techniques for identifying patterns of interdependency, generally called dimensions and types, in data. A decade ago these techniques-principal components analysis, factor analysis, and cluster analysis-were used only rarely in public administration research. The situation is quite different today, inspired not only in part by developments in doctoral training in public administration, but also by the increasing recognition of heterogeneity in the key concerns within public administration. The success of efforts to promote citizen participation in government may require understanding differences among public administrators, including a dimension that captures the degree to which public administrators trust citizens (Yang, 2005), and amidst the variation in state approaches to welfare policy, general types can be identified (Meyers, Gornick, and Peck, 2001).
