The purpose of this study is to identify what detail design tools are more productive for the different types of professional software developers. By establishing a match between the detail design tool and the cognitive style of the professional programmer, the end product (Information Systems) should be of a higher quality. Two laboratory experiments were conducted. The first experiment was with professional Software Developers; the second one was with students. The dependant variables considered in this study were the number of semantic errors and the time required to complete a design task for conditional logic. The independent variables were the cognitive style of the subject, the complexity of the task, and the detail design tools. Decision trees, flowcharts and pseudocode were used as detailed design tools. Field dependence was the only dimension of cognitive style that was tested.

This research examined the performance of three parametric methods for confidence intervals: the classical, the Bonferroni, and the bootstrap-t method, as applied to estimating the mean of voucher populations in accounting. Usually auditing populations do not follow standard models. The population for accounting audits generally is a nonstandard mixture distribution in which the audit data set contains a large number of zero values and a comparatively small number of nonzero errors. This study assumed a situation in which only overstatement errors exist. The nonzero errors were assumed to be normally, exponentially, and uniformly distributed. Five indicators of performance were used. The classical method was found to be unreliable. The Bonferroni method was conservative for all population conditions. The bootstrap-t method was excellent in terms of reliability, but the lower limit of the confidence intervals produced by this method was unstable for all population conditions. The classical method provided the shortest average width of the confidence intervals among the three methods. This study provided initial evidence as to how the parametric bootstrap-t method performs when applied to the nonstandard distribution of audit populations of line items. Further research should provide a reliable confidence interval for a wider variety of accounting populations.