Anchoring and Adjustment In Probabilistic Inference in Auditing
Journal of Accounting Research, Vol. 19, No. 1, Spring 1981
26 Pages Posted: 16 Sep 2010 Last revised: 21 Sep 2015
Date Written: 1981
Abstract
Auditors are faced with the task of formulating opinions about the fairness of their clients' financial statements. In doing so, they use their professional judgment to determine the type and amount of information to collect, the timing and manner of collecting it, and the implications of the information collected. This information is rarely, if ever, perfectly reliable or perfectly predictive of the "true" state of a client's financial statements. Nevertheless, auditors may be held liable at common law or under the federal securities laws should the audited financial statements prove to be unrepresentative of this true state. Thus, it is important for auditors to have the ability to formulate appropriately judgments based on probabilistic data.
In this paper, we describe the results of experiments designed to assess whether auditors formulate judgments in accordance' with normative principles of decision making or whether a particular alternative to the normative model of decision making under uncertainty 's employed. In the next section, we discuss several alternatives to normative decision models, focusing on the anchoring and adjustment heuristic which forms the basis for our experiments.
JEL Classification: M4
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
By Hun-tong Tan and Alison Kao
-
Are Auditors' Judgments Sufficiently Regressive?
By Edward J. Joyce and Gary C. Biddle
-
By Joseph F. Brazel, Christopher P. Agoglia, ...
-
A Comparative Evaluation of Belief Revision Models in Auditing
By Ganesh Krishnamoorthy, Theodore J. Mock, ...
-
Using Electronic Audit Workpaper Systems in Audit Practice: Task Analysis, Learning, and Resistance
By Jean C. Bedard, Michael Ettredge, ...
-
The Influence of Audit Structure on Auditors' Performance in High and Low Complexity Task Settings
By Iris Stuart and Douglas F. Prawitt
-
The Effect of Stopping Rules on the Evaluation of Audit Evidence