It remains uncertain how the Department for Work and Pensions (DWP) will manage the housing costs element of Universal Credit without increased risks of fraud and error. The Government has stated that an IT system (the Integrated Risk and Intelligence Service (IRIS)) will allow it to cross-check data and provide similar safeguards against fraudulent claims under Universal Credit as are currently operated by local authorities within the Housing Benefit system. However, the National Audit Office found that IRIS was 'missing' from the UC Pathfinders, and it remains unclear how or when DWP will achieve automated access to the range of property data currently available to local authorities. The official estimated benefit fraud rate is 0.7% of total benefits expenditure. The general public's misperception is that it is some 34 times higher. To reduce the risk of confusion or conflation in media reporting, DWP should publish statistics relating to the estimated level of benefit fraud on a separate day from those related to error in the benefits system. Fraud and error rates have plateaued from 2005/06 to 2012/13 and DWP must employ innovative approaches which are aligned with the known risk factors associated with each benefit to achieve reductions. Biometric identity systems could have an important role to play in identity verification processes across government. The Single Fraud Investigation Service (SFIS), which will investigate all social security benefit fraud across DWP, HMRC and local authorities, should be implemented in line with the roll out of Universal Credit.

Access to Work (AtW) is an important element of specialist employment support for disabled people. It is unique in providing help to people already in, or about to start, mainstream work. It has the potential to be an extremely effective model, helping to address the substantial gap between the employment rate for disabled people and that of the rest of the population. Where it works well, it transforms the lives of disabled people, many of whom would be unable to work without it.There is strong evidence that AtW currently supports only a minority of disabled people whom it might benefit. There is a misperception that the sole purpose of AtW is to provide physical aids, equipment and transport for people with sensory impairments and physical disabilities; consequently relatively few people with other types of disability, and different support needs, currently use the programme. In scaling up the programme DWP needs to address this imbalance. Its priority should be supporting a much greater number of people with mental health problems, and intellectual, cognitive and developmental impairments, including learning disabilities and autism spectrum disorders. AtW's focus should remain on removing barriers to employment for the full range of disabled people who can benefit from it. DWP should make a strong and evidence-based case to HM Treasury for substantial additional funding for AtW and then aim to increase take-up through much more high profile marketing, and proactive promotion of AtW, including through Jobcentre Plus Work Coaches and contracted employment services providers.

This is a book of hope at a time when just about everyone but Marvin Olasky has lost hope. The topic is poverty and the underclass. The profound truth that Marvin Olasky forces us to confront is that the problems of the underclass are not caused by poverty. Some of them are exacerbated by poverty, but we know that they need not be caused by poverty, for poverty has been the condition of the vast majority of human communities since the dawn of history, and they have for the most part been communities of stable families, nurtured children, and low crime. It is wrong to think that writing checks will end the problems of the underclass, or even reduce them. - Preface.

Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.

The idea of The Fingerprint Sourcebook originated during a meeting in April 2002. Individuals representing the fingerprint, academic, and scientific communities met in Chicago, Illinois, for a day and a half to discuss the state of fingerprint identification with a view toward the challenges raised by Daubert issues. The meeting was a joint project between the International Association for Identification (IAI) and West Virginia University (WVU). One recommendation that came out of that meeting was a suggestion to create a sourcebook for friction ridge examiners, that is, a single source of researched information regarding the subject. This sourcebook would provide educational, training, and research information for the international scientific community.

Welfare conditionality has become an idea of global significance in recent years. A ‘hot topic’ in North America, Australia, and across Europe, it has been linked to austerity politics, and the rise of foodbanks and destitution. In the Global South, where publicly funded welfare protection systems are often absent, conditional approaches have become a key tool employed by organisations pursuing human development goals. The essence of welfare conditionality lies in requirements for people to behave in prescribed ways in order to access cash benefits or other welfare support. These conditions are typically enforced through benefit ‘sanctions’ of various kinds, reflecting a new vision of ‘welfare’, focused more on promoting ‘pro-social’ behaviour than on protecting people against classic ‘social risks’ like unemployment. This new book in Routledge’s Key Ideas series charts the rise of behavioural conditionality in welfare systems across the globe, its appeal to politicians of Right and Left, and its application to a growing range of social problems. Crucially it explores why, in the context of widespread use of conditional approaches as well as apparently strong public support, both the efficacy and the ethics of welfare conditionality remain so controversial. As such, Welfare Conditionality is essential reading for students, researchers, and commentators in social and public policy, as well as those designing and implementing welfare policies.

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.