Interrupted Time Series Analysis

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.