Cross-sectional time-series experiments: some suggested statistical analyses.
DK Simonton - Psychological Bulletin, 1977 - psycnet.apa.org
Psychological Bulletin, 1977•psycnet.apa.org
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 …
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 …
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 GE Box and GM Jenkins (1970) approach. Some of the special advantages of the designs are briefly discussed.(22 ref)(PsycINFO Database Record (c) 2016 APA, all rights reserved)