This publication identifies interactions between agricultural and social protection interventions and shows the positive impacts of combined programmes on income diversification, food security and poverty reduction.

This volume provides a single collection some of the best articles on social experimentation and program evaluation that have appeared in the Journal of Policy Analysis and Management (JPAM). Provides exposure to a variety of well-executed social experiments and evaluations for evidence-based public policy Examines the theory and conduct of evaluations and social experiments as they relate to their practical implementation in evidence-based policy making Provides exposure to the fundamental issues surrounding the conduct of evaluations as well as to the relative merits of social experiments and the ethics and use of evaluations

Policy analysis has grown increasingly reliant on the random assignment experiment—a research method whereby participants are sorted by chance into either a program group that is subject to a government policy or program, or a control group that is not. Because the groups are randomly selected, they do not differ from one another systematically. Therefore any differences between the groups at the end of the study can be attributed solely to the influence of the program or policy. But there are many questions that randomized experiments have not been able to address. What component of a social policy made it successful? Did a given program fail because it was designed poorly or because it suffered from low participation rates? In Learning More from Social Experiments, editor Howard Bloom and a team of innovative social researchers profile advancements in the scientific underpinnings of social policy research that can improve randomized experimental studies. Using evaluations of actual social programs as examples, Learning More from Social Experiments makes the case that many of the limitations of random assignment studies can be overcome by combining data from these studies with statistical methods from other research designs. Carolyn Hill, James Riccio, and Bloom profile a new statistical model that allows researchers to pool data from multiple randomized-experiments in order to determine what characteristics of a program made it successful. Lisa Gennetian, Pamela Morris, Johannes Bos, and Bloom discuss how a statistical estimation procedure can be used with experimental data to single out the effects of a program's intermediate outcomes (e.g., how closely patients in a drug study adhere to the prescribed dosage) on its ultimate outcomes (the health effects of the drug). Sometimes, a social policy has its true effect on communities and not individuals, such as in neighborhood watch programs or public health initiatives. In these cases, researchers must randomly assign treatment to groups or clusters of individuals, but this technique raises different issues than do experiments that randomly assign individuals. Bloom evaluates the properties of cluster randomization, its relevance to different kinds of social programs, and the complications that arise from its use. He pays particular attention to the way in which the movement of individuals into and out of clusters over time complicates the design, execution, and interpretation of a study. Learning More from Social Experiments represents a substantial leap forward in the analysis of social policies. By supplementing theory with applied research examples, this important new book makes the case for enhancing the scope and relevance of social research by combining randomized experiments with non-experimental statistical methods, and it serves as a useful guide for researchers who wish to do so.

This Handbook uses methodologies and cases to discover how and when to evaluate social policy, and looks at the possible impacts of evaluation on social policy decisions. The contributors present a detailed analysis on how to conduct social policy evaluation, how to be aware of pitfalls and dilemmas and how to use evidence effectively.

The early 2020s have been marked by a surge of interest in artificial intelligence (AI), and it has grown to be one of the hottest topics in computer science, business technology research, and educational technologies. Despite AI winters in the 1970s and 1990s, where interest and subsequently adequate funding for AI research ceased, and as the technology and its usefulness become more perceptible, often with brilliant results, society is once again ready to investigate this powerful technology and its potential. However, a challenge arises when AI is called into question in an ethical context. It is important that we explore how it can contribute to the resolution of ethical, social, and environmental issues and also to address growing concerns around AI developing emergent bias as well as the human application of AI for malicious purposes. With recent AI-based writing technologies, concerns around academic integrity abound and challenge our perceptions of authenticity in writing. A careful assessment of these technologies, their usefulness and potential harm, and strategic solutions to maintaining ethical standards and regulation of the technology is a necessity for the maintenance of civilized life amidst these tools. Philosophy of Artificial Intelligence and Its Place in Society evaluates various aspects of artificial intelligence including the range of technologies, their advantages and disadvantages, and how AI systems operate. Spanning from machine learning to deep learning, philosophical insights, societal concerns, and the newest approaches to AI, it helps to develop an appreciation for and breadth of knowledge across the full range of AI sub-disciplines including neural networks, evolutionary computation, computer vision, robotics, expert systems, speech processing, and natural language processing. Led Dr. Luiz Moutinho of the University of Suffolk in the United Kingdom, who has won several awards for his academic literature, this book provides academic market-scholars; researchers and students of philosophy, sociology, economics, and education; as well as corporate scientists with a comprehensive collection of core research elements, concepts, advances, applications, evidence, and outcomes related to artificial intelligence.

This book provides accessible treatment to state-of-the-art approaches to analyzing longitudinal studies. Comprehensive coverage of the most popular analysis tools allows readers to pick and choose the techniques that best fit their research. The analyses are illustrated with examples from major longitudinal data sets including practical information about their content and design. Illustrations from popular software packages offer tips on how to interpret the results. Each chapter features suggested readings for additional study and a list of articles that further illustrate how to implement the analysis and report the results. Syntax examples for several software packages for each of the chapter examples are provided at www.psypress.com/longitudinal-data-analysis. Although many of the examples address health or social science questions related to aging, readers from other disciplines will find the analyses relevant to their work. In addition to demonstrating statistical analysis of longitudinal data, the book shows how to interpret and analyze the results within the context of the research design. The methods covered in this book are applicable to a range of applied problems including short- to long-term longitudinal studies using a range of sample sizes. The book provides non-technical, practical introductions to the concepts and issues relevant to longitudinal analysis. Topics include use of publicly available data sets, weighting and adjusting for complex sampling designs with longitudinal studies, missing data and attrition, measurement issues related to longitudinal research, the use of ANOVA and regression for average change over time, mediation analysis, growth curve models, basic and advanced structural equation models, and survival analysis. An ideal supplement for graduate level courses on data analysis and/or longitudinal modeling taught in psychology, gerontology, public health, human development, family studies, medicine, sociology, social work, and other behavioral, social, and health sciences, this multidisciplinary book will also appeal to researchers in these fields.

The Impact of Corporate Social Responsibility: Corporate Activities, the Environment and Society adds to the current debate on the societal-level impacts of corporate social responsibility (CSR). This edited volume offers conceptual and empirical contributions highlighting various dimensions of CSR impacts. What differentiates the book from others is that we examine the impact of CSR at the societal level, rather than focussing only on those at occur at the level of the firm. The book’s contributions present novel perspectives that comprise, among others, empirical analyses of CSR activities, accounts of impacts in various geographic locations, and state-of-the-art reviews of extant literature on the topic. The practical examples and theory-building presented here help us to better capture the societal impacts of contemporary CSR practice. This book will appeal to scholars and students as well as practitioners and policy makers interested in practical and theoretical aspects of CSR impacts at the societal-level. Chapter 3 of this book is freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives CC-BY 4.0 license.

Social isolation and loneliness are serious yet underappreciated public health risks that affect a significant portion of the older adult population. Approximately one-quarter of community-dwelling Americans aged 65 and older are considered to be socially isolated, and a significant proportion of adults in the United States report feeling lonely. People who are 50 years of age or older are more likely to experience many of the risk factors that can cause or exacerbate social isolation or loneliness, such as living alone, the loss of family or friends, chronic illness, and sensory impairments. Over a life course, social isolation and loneliness may be episodic or chronic, depending upon an individual's circumstances and perceptions. A substantial body of evidence demonstrates that social isolation presents a major risk for premature mortality, comparable to other risk factors such as high blood pressure, smoking, or obesity. As older adults are particularly high-volume and high-frequency users of the health care system, there is an opportunity for health care professionals to identify, prevent, and mitigate the adverse health impacts of social isolation and loneliness in older adults. Social Isolation and Loneliness in Older Adults summarizes the evidence base and explores how social isolation and loneliness affect health and quality of life in adults aged 50 and older, particularly among low income, underserved, and vulnerable populations. This report makes recommendations specifically for clinical settings of health care to identify those who suffer the resultant negative health impacts of social isolation and loneliness and target interventions to improve their social conditions. Social Isolation and Loneliness in Older Adults considers clinical tools and methodologies, better education and training for the health care workforce, and dissemination and implementation that will be important for translating research into practice, especially as the evidence base for effective interventions continues to flourish.