Interrupted time series analysis, Issues 21-25
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 fro
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2SE 2SE Figure abrupt ACF and PACF ACF(k ACF(l ARIMA model ARIMA(p,d,q AUTOCORRELATIONS OF LAGS autoregressive process behavior bounds of invertibility bounds of stationarity change in level co(l correlation covariance(YtYt+1 decay differenced series different than white Directory Assistance event example expected PACF filters Hyde Park purse identification impact assessment model indicate interrupted time series intervention component IXXX IXXXXX LAG CORR MINITAB model residuals model-building strategy moving average processes NOBS noise component nonzero null hypothesis PACF estimated parameter estimates PARTIAL AUTOCORRELATIONS pattern of impact postintervention observation preintervention pulse function Q statistic reader regularly and seasonally seasonal lag seasonally differenced series analysis series Figure series observation series process series quasi-experiment shows the ACF social science spikes statistical conclusion validity statistically significant stochastic Sutter County Workforce techniques tically trend VALID OBSERVATIONS variance white noise written as Yt XXXI XXXXXI XXXXXXXXXXX yt series yt time series zero