Modeling External Auditors' Evaluations of Internal Auditing

Recent government actions1 have led to an increase in the number of companies having internal audit (IA) departments and in the average size of IA staffs (Macchiaverna [1978]). This development has had an impact on the audits made by external auditors who generally rely on the work performed by the internal auditors. A survey by Ward and Robertson [1980] showed that "virtually all independent auditors rely on internal auditors to some extent" and that this reliance should increase in the future. The significance of the IA function in the internal control system has often been pointed out by external auditors. Research by Ward [1979] indicated that external auditors believe the IA function "should be viewed as an integral part of the internal control system rather than merely a check on that system" and that "external audit costs should usually be materially less when internal auditors are relied upon than what they would have been without reliance." In order to make a decision about the extent of reliance on the work of internal auditors, the auditor2 must judge the strength of the IA

[1]  D. Emery.  A Monte Carlo investigation of scaling as an alternative to regression in the bootstrapping model , 1978 .

[2]  A. Tversky,et al.  Conjoint-measurement analysis of composition rules in psychology. , 1971 .

[3]  D. Emery,et al.  Axiomatic and numerical conjoint measurement: An evaluation of diagnostic efficacy , 1979 .

[4]  G. W. Fischer,et al.  Multidimensional utility models for risky and riskless choice , 1976 .

[5]  Herbert H. Stenson,et al.  GOODNESS OF FIT FOR RANDOM RANKINGS IN KRUSKAL'S NONMETRIC SCALING PROCEDURE * , 1969 .

[6]  Paul R. Brown.  INDEPENDENT AUDITOR JUDGMENT IN THE EVALUATION OF INTERNAL AUDIT FUNCTIONS , 1983 .

[7]  Bruce L. Stern,et al.  Research for Marketing Decisions , 1978 .

[8]  J. Kruskal.  Analysis of Factorial Experiments by Estimating Monotone Transformations of the Data , 1965 .

[9]  Comment on a Monte Carlo investigation of scaling as an alternative to regression , 1979 .

[10]  William F. Messier,et al.  SOME CAUTIONARY NOTES ON THE USE OF CONJOINT MEASUREMENT FOR HUMAN JUDGMENT MODELING , 1980 .

[11]  T. Wallsten.  Conjoint-Measurement Framework for the Study of Probabilistic Information Processing. , 1972 .

[12]  S. Moriarity,et al.  Modeling the Materiality Judgements of Audit Partners , 1976 .

[13]  Conjoint-Measurement And The Analysis Of Noisy Data - A Comment , 1982 .

[14]  Clyde H. Coombs,et al.  Polynomial psychophysics of risk , 1970 .

[15]  L. R. Goldberg.  Simple models or simple processes? Some research on clinical judgments. , 1968, The American psychologist.

[16]  Edward J. Joyce,et al.  Expert Judgment In Audit Program Planning , 1976 .

[17]  F. H. Barron,et al.  AXIOMATIC CONJOINT MEASUREMENT , 1977 .

[18]  Paul E. Green,et al.  On the Analysis of Interactions in Marketing Research Data , 1973 .