Abstract
This study examines the implications of using national-level (i.e., audit firm-wide) data aggregated across individual practice offices when testing for quality differentiation in auditing. I present a statistical framework that identifies the nature and sources of bias in aggregation when important heterogeneity exists within aggregate units, and then subject it to empirical analysis. I find, as predicted, a tendency for aggregation to aggravate omitted variables bias, leading to serious overestimation of the effect of clientele size on audit quality. I also evaluate an approach to minimize the potential bias in tests that use aggregate data. My analysis provides the necessary background for researchers to choose among alternative levels of aggregation in audit research.
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