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Journal Article 1: Pontell, H. N., & Geis, G. (2014). The trajectory of white-collar crime following the great economic meltdown. Journal of Contemporary Criminal Justice, 30, 70–82.

Abstract: This article reviews issues in white-collar crime research related to both the causes and aftermath of the 2008 economic meltdown. The absence of major prosecutions, new legislation designed to prevent similar debacles, and post-meltdown case studies are discussed to illuminate the culture and business practices of major companies that were responsible for the unprecedented losses to the financial system and the resulting taxpayer bailout. We conclude that despite new legislation, regulatory gaps and continued lack of adequate oversight as a result of intense lobbying by the financial industry, as well as a non-understanding of the role of fraud in major financial debacles by policy makers, virtually guarantee that future financial crises will occur due to fraud, even in the most elite firms.

Journal Article 2: Addington, L. A. (2008). Assessing the extent of nonresponse bias on NIBRS estimates of violent crime. Journal of Contemporary Criminal Justice, 24, 32–49.

Abstract: More than two decades ago, the U.S. Department of Justice released its recommendations for a new data collection method for the Uniform Crime Reporting Program that would become the National Incident-Based Reporting System (NIBRS). The full potential of NIBRS data has not been realized based on assumptions that these data are inherently biased due to a lack of national coverage and overrepresentation by law enforcement agencies serving smaller populations. To date, no research has assessed the amount of nonresponse or the degree to which NIBRS data are biased for generating crime estimates. Although this study confirms an overall low response rate for NIBRS reporting, higher rates are found for particular subnational population groups. An examination of violent crime rates and change estimates suggests that the amount of bias in NIBRS is not so small as to be ignorable but is not so considerable as to warrant abandoning these data altogether.