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|Title:||A Multivariate Analysis of Auditor Decision Making in the Presence of Going-Concern Uncertainties|
|Author(s):||Mutchler, Jane Frances|
|Department / Program:||Accountancy|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Subject(s):||Business Administration, Accounting|
|Abstract:||The purpose of this research was to determine how an auditor identifies a company with a potential going-concern problem and, given that identification, how an auditor decides whether a going-concern opinion should be issued. The focus for studying the opinion decision was on the relationship between financial statement variables and the opinion issued. Information about specific variables used for the opinion decision was gained through interviews with auditors and questionnaires answered by those same subjects. These variables, including ratios, SAS No. 34 variables (contrary information and mitigating factors) and other variables deemed important by auditors, were inputs into the modelling phase of the research.
Classification accuracy results of the models tested reflect the pooled results across ten separate discriminant runs, each of which used a separate randomly chosen derivation and validation sample. Results were restated to reflect what could be expected on a random sample from the population and were compared to an appropriate chance model. An outlier identification and truncation procedure was followed and classification accuracy was assessed with the variables in the raw-truncated form and again in the least-skewed truncated form.
The auditor-chosen set of ratios resulted in higher classification accuracy (85.6% for the raw-truncated form and 84.6% for the least skewed truncated form) than any model tested. An analysis of the expected misclassification costs for this auditor ratio model using both equal and proportional prior probabilities of group membership was conducted. The results were compared to appropriate chance models. An analysis was also conducted on the companies that were misclassified across all models tested. Information is provided on the problem characteristics and auditor affiliation of these misclassified companies.
A secondary purpose of the research was to replicate the modelling phase of the Kida (1980) research. A current sample of companies that met his sample criteria was used. A 76.7% accuracy rate was obtained for the current sample versus a 90.0% rate for the original.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1983.
|Date Available in IDEALS:||2014-12-16|