Irrelevant Autocorrelation in Least-Squares Intervention Models

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Abstract

Autocorrelation coefficients computed on the entire series of observations obtained from interrupted time-series designs are generally irrelevant to the general linear model (GLM) independence assumption. Consequently, the argument that GLM solutions are invalidated when large coefficients of this type are encountered is incorrect. Several decompositions of the terms involved in such coefficients are provided to show how deterministic components in the correct model contaminate these coefficients. Example data sets from articles in the methodological literature that were written to promote the use of complex time-series methods are used to illustrate relevant and irrelevant autocorrelations and to demonstrate the application and viability of GLM time-series intervention models.

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