David McDowall's ground-breaking history of the Kurds from the 19th century to the present day documents the underlying dynamics of the Kurdish question. The division of the Kurdish people among the modern nation states of Iraq, Turkey, Syria and Iran and their struggle for national rights continues to influence the politics of the Middle East. Drawing extensively on primary sources - including documents from The National Archive and interviews with prominent Kurds - the book examines the interplay of old and new aspects of the struggle, the importance of local rivalries and leadership within Kurdish society, and the failure of modern states to respond to the challenge of Kurdish nationalism. In this new and revised edition, McDowall also analyses the momentous transformations affecting Kurdish socio-politics in the last 20 years. With updates throughout and substantial new material included, this fourth edition of the book reflects the developments in the field and the areas which have gained importance and understanding. This includes new analysis of the Kurdish experience in Syria; the role of political Islam in Kurdish society and Kurds' involvement in Islamist Jihad; and issues surrounding women and gender that were previously overlooked, from the impact of the women's equality movement to how patriarchal practices within the Kurdish community still limit its progress. The foundation text for Kurdish Studies, this book highlights in detail the changing situation of the Kurds across the Middle East.

Design and Analysis of Time Series Experiments presents the elements of statistical time series analysis while also addressing recent developments in research design and causal modeling. A distinguishing feature of the book is its integration of design and analysis of time series experiments. Drawing examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, Design and Analysis of Time Series Experiments is addressed to researchers and graduate students in a wide range of behavioral, biomedical and social sciences. Readers learn not only how-to skills but, also the underlying rationales for the design features and the analytical methods. ARIMA algebra, Box-Jenkins-Tiao models and model-building strategies, forecasting, and Box-Tiao impact models are developed in separate chapters. The presentation of the models and model-building assumes only exposure to an introductory statistics course, with more difficult mathematical material relegated to appendices. Separate chapters cover threats to statistical conclusion validity, internal validity, construct validity, and external validity with an emphasis on how these threats arise in time series experiments. Design structures for controlling the threats are presented and illustrated through examples. The chapters on statistical conclusion validity and internal validity introduce Bayesian methods, counterfactual causality and synthetic control group designs. Building on the earlier of the authors, Design and Analysis of Time Series Experiments includes more recent developments in modeling, and considers design issues in greater detail than any existing work. Additionally, the book appeals to those who want to conduct or interpret time series experiments, as well as to those interested in research designs for causal inference.

Alarming news reports point to an almost incomprehensible problem of violence in America. Understanding this problem requires timely and accurate information about the magnitude and scope of violence, the effect of violence on our society, and society's perceptions of violence. "Statistical Handbook on Violence in America" is the authoritative source of data gathered from widely scattered sources, both published and unpublished, and assembled in a single volume for accurate and efficient access. Featuring 377 tables and figures, this volume reveals data on victims and offenders, as well as the association of violence with: the home, health care, individual attitudes, the workplace, the economy, and public policy issues.

David McDowall's ground-breaking history of the Kurds from the 19th century to the present day documents the underlying dynamics of the Kurdish question. The division of the Kurdish people among the modern nation states of Iraq, Turkey, Syria and Iran and their struggle for national rights continues to influence the politics of the Middle East. Drawing extensively on primary sources - including documents from The National Archive and interviews with prominent Kurds - the book examines the interplay of old and new aspects of the struggle, the importance of local rivalries and leadership within Kurdish society, and the failure of modern states to respond to the challenge of Kurdish nationalism. In this new and revised edition, McDowall also analyses the momentous transformations affecting Kurdish socio-politics in the last 20 years. With updates throughout and substantial new material included, this fourth edition of the book reflects the developments in the field and the areas which have gained importance and understanding. This includes new analysis of the Kurdish experience in Syria; the role of political Islam in Kurdish society and Kurds' involvement in Islamist Jihad; and issues surrounding women and gender that were previously overlooked, from the impact of the women's equality movement to how patriarchal practices within the Kurdish community still limit its progress. The foundation text for Kurdish Studies, this book highlights in detail the changing situation of the Kurds across the Middle East.

Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. Additionally, the book supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. Not only does the text discuss new developments, including the prospects for widespread adoption of Bayesian hypothesis testing and synthetic control group designs, but it makes optimal use of graphical illustrations in its examples. With forty completed example analyses that demonstrate the implications of model properties, Interrupted Time Series Analysis will be a key inter-disciplinary text in classrooms, workshops, and short-courses for researchers familiar with time series data or cross-sectional regression analysis but limited background in the structure of time series processes and experiments.

Containing information regarding the geology, ecology, and history of the terrain, this walkers guide to Richmond Park includes both its political history (its medieval and post medieval ownership) and also its socio-economic history - how its natural products have been used over the centuries.

Design and Analysis of Time Series Experiments presents the elements of statistical time series analysis while also addressing recent developments in research design and causal modeling. A distinguishing feature of the book is its integration of design and analysis of time series experiments. Drawing examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, Design and Analysis of Time Series Experiments is addressed to researchers and graduate students in a wide range of behavioral, biomedical and social sciences. Readers learn not only how-to skills but, also the underlying rationales for the design features and the analytical methods. ARIMA algebra, Box-Jenkins-Tiao models and model-building strategies, forecasting, and Box-Tiao impact models are developed in separate chapters. The presentation of the models and model-building assumes only exposure to an introductory statistics course, with more difficult mathematical material relegated to appendices. Separate chapters cover threats to statistical conclusion validity, internal validity, construct validity, and external validity with an emphasis on how these threats arise in time series experiments. Design structures for controlling the threats are presented and illustrated through examples. The chapters on statistical conclusion validity and internal validity introduce Bayesian methods, counterfactual causality and synthetic control group designs. Building on the earlier of the authors, Design and Analysis of Time Series Experiments includes more recent developments in modeling, and considers design issues in greater detail than any existing work. Additionally, the book appeals to those who want to conduct or interpret time series experiments, as well as to those interested in research designs for causal inference.

Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. It provides example analyses of social, behavioral, and biomedical time series to illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. Additionally, the book supplements the classic Box-Jenkins-Tiao model-building strategy with recent auxiliary tests for transformation, differencing, and model selection. Not only does the text discuss new developments, including the prospects for widespread adoption of Bayesian hypothesis testing and synthetic control group designs, but it makes optimal use of graphical illustrations in its examples. With forty completed example analyses that demonstrate the implications of model properties, Interrupted Time Series Analysis will be a key inter-disciplinary text in classrooms, workshops, and short-courses for researchers familiar with time series data or cross-sectional regression analysis but limited background in the structure of time series processes and experiments.

Describes ARIMA, or Box-Tiao models, widely used in the analysis of interrupted time series quasi-experiments. Assumes no statistical background beyond simple correlation.