Interrupted Time Series Analysis

David McDowall, Richard McCleary, and Bradley J. Bartos

Provides tools for researchers whose data consist of a long sequence of observations measured before and after a treatment, intervention, or other significant change
Integrates the statistical issues of design, estimation and interpretation with foundational validity issues
Provides a unique focus on time series experiments
Emphasizes graphical aspects of time series analysis, including more than 130 figures and an equal number of tables
Includes over forty example analyses that instructors could assign as problems to students

Interrupted Time Series Analysis

David McDowall, Richard McCleary, and Bradley J. Bartos

Description

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.

Interrupted Time Series Analysis

David McDowall, Richard McCleary, and Bradley J. Bartos

Interrupted Time Series Analysis

David McDowall, Richard McCleary, and Bradley J. Bartos

Author Information

David McDowall, Distinguished Teaching Professor, School of Criminal Justice, University at Albany, State University of New York, Richard McCleary, Professor of Criminology, Law, and Society and Planning, Policy, and Design, University of California, Irvine, and Bradley J. Bartos, Ph.D. Candidate, School of Social Ecology at the University of California, Irvine

David McDowall is Distinguished Teaching Professor at the University at Albany, State University of New York. He serves on the faculty of Albany's School of Criminal Justice, where he also co-directs the Violence Research Group. His research interests involve the social distribution of criminal violence, including trends and other temporal features in crime rates. Richard McCleary is a professor at the University of California, Irvine. In addition to faculty appointments in Criminology, Law and Society, Environmental Health Sciences, and Planning, Policy and Design, he directs the Irvine Simulation Modeling Laboratory. His research interests include population forecast models, time series models, and survival models. Bradley J. Bartos is a doctoral candidate in the Department of Criminology, Law and Society at the University of California, Irvine. Through his work with the Irvine Simulation Modeling Laboratory, he has developed discrete-event population projection models for

various criminal-justice and corrections systems in California. His research interests include mass incarceration, policy evaluation, time series models, and synthetic control group designs.