According to SAS No. 56, Analytical Procedures, the use of disaggregate, in
dividual location data can improve the effectiveness of analytical procedur
es used in multilocation audits. Using a case-study approach, we investigat
e whether improvements in the accuracy and precision of account balance exp
ectations can be obtained by using disaggregate, individual location data i
n a large, multilocation company. Specifically, we examine two issues: (1)
whether the summation of individual location expectations generates more ac
curate and precise expectations of company-wide account balances than expec
tations based on company-wide data only and (2) whether the accuracy and pr
ecision of analytical procedures is enhanced by including peer location obs
ervations of the account balance in individual location expectation models.
We find that for the multilocation company examined in this case study the
summation of individual location account balance expectations is not more a
ccurate or precise than an expectation derived from aggregate models unless
the individual location models include peer location observations of the a
ccount balance. When the individual location models include the same accoun
t observations from other peer locations within the company, the company-wi
de account balance expectations developed from disaggregate models are more
accurate and precise (less variable) than expectations developed using agg
regate, company-wide data only. The results from this case study indicate t
hat when auditors are generating expectations of company-wide balances, dis
aggregate models incorporating peer location account observations provide a
ccount balance expectations that are both more accurate and more precise th
an company-wide, aggregate models. Given the limitations of a case-study ap
proach, future research should be directed at establishing the generalizabi
lity of these findings.