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. Readers learn not only how-to skills but also the underlying rationales for design features and 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 time series books by McCleary and McDowall, Design and Analysis of Time Series Experiments includes recent developments in modeling, and considers design issues in greater detail than does any existing work. Drawing examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, the text is addressed to researchers and graduate students in a wide range of behavioral, biomedical and social sciences. It will appeal to those who want to conduct or interpret time series experiments, as well as to those interested in research designs for causal inference. "This is a wonderful book for anyone especially interested in interrupted time-series design and analysis. The presentation is very clear, the main design and analysis options are comprehensive, and the advice that careful reading will elicit is invariably wise. I will recommend it to anyone in the social and behavioral sciences who wants a comprehensive and accessible introduction to what interrupted time series can achieve in the hands of those willing to trust this book." --Thomas D. Cook, Joan and Serepta Harrison Emeritus Professor of Ethics and Justice and Professor Emeritus of Sociology, Psychology, Education, and Social Policy, Northwestern University "McCleary, McDowall, and Bartos have advanced a field of research methodology that has lain fallow for more than three decades; and in the process, they challenge staid thinking in the area of verification of causal claims in disciplines as diverse as social science, economics, and medicine." --Gene V. Glass, National Education Policy Center, University of Colorado "McCleary, McDowall, and Bartos have truly extended our knowledge of time series methodology. And of special note, time series methodology is foundational for single-case intervention research and the causal inference associated with this unique methodology. McCleary and his fellow scientists have offered single-case researchers new and important insights into experimental and quasi-experimental causal inference. Most importantly, single-case design researchers will find new conceptual features of the time series validities that will further promote the credibility of this methodology in the social and behavioral sciences. I strongly recommend this important resource to my single-case research colleagues." --Thomas R. Kratochwill, Sears-Bascom Professor of School Psychology, University of Wisconsin-Madison "Time series data are ubiquitous nowadays. The innovations of this book relative to other time series texts are its integration of contemporary counterfactual approaches to causal inference with long-standing interrupted time series methods of analysis, its incorporation of Bayesian statistical inference, and its thorough treatment of threats to the internal, external, and construct validity of time series analyses." --Kenneth C. Land, John Franklin Crowell Professor Emeritus of Sociology, Duke University " Design and Analysis of Time Series Experiments gives a new generation of behavioral, biomedical, and social scientists a comprehensive resource for understanding time series issues of causality, validity, and experimental design. Drawing on time series data about social problems such as homicides, public drunkenness, speeding crackdowns, and self-injurious behaviour, the authors present complex statistical concepts and issues in an exceptionally accessible style that will surely make it a favourite, go-to book for analysts for many years to come." --Lorraine Mazzerole, Professor, School of Social Science, University of Queensland, Australia "Time-series designs are critically important for rigorous scientific evaluation of the whole panoply of laws, public policies, and s