Interrupted Time Series Analysis

By: David McDowall, Richard McCleary, Errol Meidinger, Richard A. Hay

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Paperback | 1 August 1980

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96 Pages

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21.59 x 13.97 x 0.61

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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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Editor's Introduction	p. 5
Introduction	p. 9
The Stochastic Component, N[subscript t]	p. 15
ARIMA (0,0,0) and ARIMA (0,d,0) Processes	p. 18
The Autocorrelation Function	p. 24
Moving Average Models	p. 27
Autoregressive Models	p. 32
The Partial Autocorrelation Function	p. 40
Mixed Autoregressive-Moving Average Models	p. 46
Model Building	p. 47
Seasonal Models	p. 54
The Intervention Component, I[subscript t]	p. 64
An Abrupt, Permanent Impact	p. 67
A Gradual, Permanent Impact	p. 75
An Abrupt, Temporary Impact	p. 80
Testing Rival Hypotheses	p. 83
Conclusion	p. 85
Appendices	p. 88
Notes	p. 91
References	p. 94
Table of Contents provided by Syndetics. All Rights Reserved.

Interrupted Time Series Analysis

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