Cross-sectional time-series experiments: Some suggested statistical analyses


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Abstract

In the past, statistical analyses for time-series experiments have usually operated with a single-case model, thereby limiting the general applicability of the designs. In this article, alternative analytical procedures are developed for cross-sectional time-series in which the sample size is large and the number of observations per case is relatively small. Interrupted time series, equivalent time samples, and multiple time series are all treated within a multiple regression framework. A generalized least squares estimation procedure is outlined as a more suitable alternative to the G. E. Box and G. M. Jenkins (1970) approach. Some of the special advantages of the designs are briefly discussed. (22 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved)

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