Regression analysis for equipment auditing
Purpose - To develop, test and implement a sampling strategy for equipment auditing for a Design/methodology/approach - Regression analysis is applied to auditing of equipment for a large US corporation. Empirical data and test data sets are used to evaluate the efficacy of using regression for auditing and to determine reasonable and efficient sample sizes to be employed across more than 5,000 locations. Findings - Regression is a viable and useful method for equipment auditing when there is anticipated high correlation between pre- and post-audit equipment value. Recommended sample size is dependent upon the size of the location as measured by total pieces of equipment. Decision rules combining acceptable tolerance limits, desired confidence level and sample size are provided. Research limitations/implications - The method, recommended sample sizes and decision rules are particularly applicable to instances where high correlation is expected between pre- and post-audit equipment values. Standard regression assumptions are not all met in all instances, especially with small sample sizes. Practical implications - The regression approach and model, sample size recommendations and decision rules for passing or failing an equipment audit described herein have been implemented at a Originality/value - This paper provides a practical and useful regression-based approach to sampling for equipment auditing. Recommended sample sizes and decision rules for passing or failing the audit are explicitly defined.
Audit sampling strategies and frauds: an evidence from Africa

Purpose
This paper aims to investigate how professionals such as accountants, auditors, senior civil servants and academics perceive the use of audit sampling strategies adopted by professionals to increase detection rates of frauds and corruption within the public sector in Africa. It also examines the respondents’ perceived values regarding the reasons for committing frauds, types of fraud and corruption, as well as the aspects of audit sampling strategies to tackle frauds.


Design/methodology/approach
This research uses non-parametric statistics and logistic regression to analyse the respondents’ opinions regarding the state of frauds and corruption in Africa (particularly in Tunisia and non-Tunisia countries), the common factors behind people committing frauds, including the types of frauds and corruption and the respondents’ opinions on the use of audit sampling strategies (non-random and random) to examine the instances of frauds and corruption.


Findings
The findings indicate that most respondents prefer to use non-probabilistic audit sampling rather than more robust sampling strategies such as random sampling and systematic random sampling to detect frauds and corruption. In addition, although there are some minor statistical differences between the countries in terms of the respondents’ perceived values on skimming fraud and on the use of audit random sampling to tackle rampant corruption in Africa, the overall findings indicate that opinions do not significantly differ between the respondents from Tunisia and other countries in terms of the types of fraud, the reasons for committing fraud and the auditing sampling strategies used to investigate the frauds.


Research limitations/implications
This research serves as an analytical exploratory study to instigate further audit sampling research to combat rampant fraud and corruption in the public sector in Africa.


Originality/value
There are few or non-existent studies investigating the application of audit sampling strategies in Africa countries, particularly to examine the application of audit random sampling and audit non-random sampling strategies to detect fraudulent activities and corruption. Correspondingly, this research carries strategic implications for accountants and auditors to successfully detect fraudulent activities and corruption in Africa.

Migration, Labor Mobility, and Relocation Assistance: The Case of the American Indian
This article, an example of "indirect" program evaluation, utilizes a 1/100 public use sample of American households from the 1970 U.S. census to examine family welfare levels among three categories of Indian families: (1) nonmigrant families living in traditional areas of Indian population concentration; (2) families in areas not receiving relocation assistance through the Bureau of Indian Affairs (BIA); and (3) migrant families moving to urban areas with BIA relocation assistance. The article concludes that the population of migrants receiving relocation assistance has a substantially higher level of economic welfare in both absolute and relative terms and that earnings are the principal source of increased welfare. It is suggested that such assistance might be utilized on a wider basis to assist other migrating populations.