The Use of Hierarchical Linear Modeling to Address Lack-of-Independence in Empirical Auditing Research

Abstract

Prior empirical auditing research has typically used linear regression analysis to analyze auditor relationships. However, because audit firms, audit partners, and audit clients are nested and clustered, data on them lacks independence, and violates the assumptions necessary for valid tests using simple linear regressions. This deficiency can be overcome by employing the hierarchical linear modeling (HLM) technique to conduct empirical tests. We illustrate this by employing HLM to explain the relationship between audit quality and audit firm, and audit partner tenure. We show that employing HLM yields different results than those found using ordinary least squares.