Interrupted Time Series Analysis

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David McDowall - SUNY, Albany, USA
Richard McCleary - University of California, Irvine, USA
Errol Meidinger
Richard A. Hay, Jr

Volume: 21

Series:
Quantitative Applications in the Social Sciences

Other Titles in:
Quantitative/Statistical Research (General)

December 1980 | 96 pages | SAGE Publications, Inc

Description

Describes ARIMA or Box Tiao models, widely used in the analysis of interupted time series quasi-experiments, assuming no statistical background beyond simple correlation. The principles and concepts of ARIMA time series analyses are developed and applied where a discrete intervention has impacted a social system.

'...this is the kind of exposition I wished I had had some ten years ago when venturing into the world of autoregressive, moving-average (ARIMA) models of time-series analysis...This monograph nicely lays out a method for assessing the impact of a discrete policy or event of some importance on behavior which can be continuously observed...If widely used, as I hope, it will save a generation of social scientists from the labor of having to learn this methodology the hard way...' -- Helmut Norpoth, State University of New York

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