There has been an increase in awareness (and perhaps occurrence) of individual and organized cheating on tests. Recent reports of widespread problems with state student accountability tests and teacher certification testing have raised questions about the very validity of assessment programs. While there are several books that specifically detail the issues of test security cheating on assessments, few outline the statistical procedures used for detecting various types of potential test fraud and the associated research findings. Without a significant research literature base, the new generation of researchers will have little opportunity or incentive to improve on existing methods. Enlisting a variety of experts and scholars in different fields of testing, this edited volume expands on the current literature base by including examples of detailed research findings arrived at by statistical methodology. It also provides a synthesis of the current state of the art with regard to the statistical detection of testing infidelity, particularly for large-scale assessments. By presenting methods currently used by testing organizations and research on new methods, the volume offers an important forum for expanding the literature in this area.

Forensic Accounting provides comprehensive coverage of fraud detection and deterrence and includes the broader educational material of the forensic accounting field with all the necessary accompaniments. The text follows the model curriculum for education in fraud and forensic funded by the U.S. national Institute of Justice and developed by a Technical Working Group of experts in the field. The text serves as a comprehensive and authoritative resource for teaching forensic accounting concepts and procedures that is also and appropriate and pedagogically ready for class room use. This easy to read, comprehensive textbook includes case study examples to clearly explain technical concepts and bring the material to life.

A powerful new tool for all forensic accountants, or anyone whoanalyzes data that may have been altered Benford's Law gives the expected patterns of the digits in thenumbers in tabulated data such as town and city populations orMadoff's fictitious portfolio returns. Those digits, in unaltereddata, will not occur in equal proportions; there is a large biastowards the lower digits, so much so that nearly one-half of allnumbers are expected to start with the digits 1 or 2. Thesepatterns were originally discovered by physicist Frank Benford inthe early 1930s, and have since been found to apply to alltabulated data. Mark J. Nigrini has been a pioneer in applyingBenford's Law to auditing and forensic accounting, even before hisgroundbreaking 1999 Journal of Accountancy article introducing thisuseful tool to the accounting world. In Benford's Law, Nigrinishows the widespread applicability of Benford's Law and itspractical uses to detect fraud, errors, and other anomalies. Explores primary, associated, and advanced tests, all describedwith data sets that include corporate payments data and electiondata Includes ten fraud detection studies, including vendor fraud,payroll fraud, due diligence when purchasing a business, and taxevasion Covers financial statement fraud, with data from Enron, AIG,and companies that were the target of hedge fund short sales Looks at how to detect Ponzi schemes, including data on Madoff,Waxenberg, and more Examines many other applications, from the Clinton tax returnsand the charitable gifts of Lehman Brothers to tax evasion andnumber invention Benford's Law has 250 figures and uses 50 interestingauthentic and fraudulent real-world data sets to explain boththeory and practice, and concludes with an agenda and directionsfor future research. The companion website adds additionalinformation and resources.

NATIONAL BESTSELLER • The gripping story of Elizabeth Holmes and Theranos—one of the biggest corporate frauds in history—a tale of ambition and hubris set amid the bold promises of Silicon Valley, rigorously reported by the prize-winning journalist. With a new Afterword covering her trial and sentencing, bringing the story to a close. “Chilling ... Reads like a thriller ... Carreyrou tells [the Theranos story] virtually to perfection.” —The New York Times Book Review In 2014, Theranos founder and CEO Elizabeth Holmes was widely seen as the next Steve Jobs: a brilliant Stanford dropout whose startup “unicorn” promised to revolutionize the medical industry with its breakthrough device, which performed the whole range of laboratory tests from a single drop of blood. Backed by investors such as Larry Ellison and Tim Draper, Theranos sold shares in a fundraising round that valued the company at more than $9 billion, putting Holmes’s worth at an estimated $4.5 billion. There was just one problem: The technology didn’t work. Erroneous results put patients in danger, leading to misdiagnoses and unnecessary treatments. All the while, Holmes and her partner, Sunny Balwani, worked to silence anyone who voiced misgivings—from journalists to their own employees.

The rising reliance on testing in American education and for licensure and certification has been accompanied by an escalation in cheating on tests at all levels. Edited by two of the foremost experts on the subject, the Handbook of Quantitative Methods for Detecting Cheating on Tests offers a comprehensive compendium of increasingly sophisticated data forensics used to investigate whether or not cheating has occurred. Written for practitioners, testing professionals, and scholars in testing, measurement, and assessment, this volume builds on the claim that statistical evidence often requires less of an inferential leap to conclude that cheating has taken place than do other, more common sources of evidence. This handbook is organized into sections that roughly correspond to the kinds of threats to fair testing represented by different forms of cheating. In Section I, the editors outline the fundamentals and significance of cheating, and they introduce the common datasets to which chapter authors' cheating detection methods were applied. Contributors describe, in Section II, methods for identifying cheating in terms of improbable similarity in test responses, preknowledge and compromised test content, and test tampering. Chapters in Section III concentrate on policy and practical implications of using quantitative detection methods. Synthesis across methodological chapters as well as an overall summary, conclusions, and next steps for the field are the key aspects of the final section.

Detect fraud faster—no matter how well hidden—with IDEA automation Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book. Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification.

High stakes tests are the gatekeepers to many educational and professional goals. As such, the incentive to cheat is high. This Handbook is the first to offer insights from experts within the testing community, psychometricians, and policymakers to identify and develop best practice guidelines for the design of test security systems for a variety of testing genres. Until now this information was scattered and often resided inside testing companies. As a result, rather than being able to learn from each other’s experiences, each testing entity was left to re-create their own test security wheel. As a whole the book provides invaluable insight into the prevalence of cheating and “best practices” for designing security plans, training personnel, and detecting and investigating misconduct, to help develop more secure testing systems and reduce the likelihood of future security breaches. Actual case studies from a variety of settings bring to life how security systems really work. Examples from both domestic and international programs are provided. Highlights of coverage include: • Best practices for designing secure tests • Analysis of security vulnerabilities for all genres of testing • Practical cheating prevention and detection strategies • Lessons learned in actual security violations in high profile testing programs. Part I focuses on how tests are delivered for paper-and-pencil, technology-based, and classroom testing and writing assessment. Each chapter addresses the prevalence of the problem and threats to security, prevention, and detection. Part II addresses issues essential to maintaining a secure testing program such as planning and monitoring, physical security, the detection of group-based cheating, investigating misconduct, and communicating about security-related issues. Part III examines actual examples of cheating-- how the cheating was done, how it was detected, and the lessons learned. Part III provides insight into security issues within each of the Association of Test Publishers’ four divisions: certification/licensure, clinical, educational, and industrial/organizational testing. Part III’s conclusion revisits the issues addressed in the case studies and identifies common themes. Intended for organizations, professionals, educators, policy makers, researchers, and advanced students that design, develop, or use high stakes tests, this book is also ideal for graduate level courses on test development, educational measurement, or educational policy.