Interrupted Time Series Analysis

Interrupted Time Series Analysis offers a focused treatment of analytic models and methods for drawing causal inferences from time series. It provides a toolbox for researchers in a number of social science disciplines, including sociology, criminology, political science, psychology, education, and public health where researcher needs to assess the impact of changes in law, policy, and major historical events on behavior, or test and measure the impact of treatments or interventions.

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.