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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.
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