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Fallability in the Visual Assessment of Behavioural Interventions: Time-Series Statistics to Analyse Time-Series Data

Published online by Cambridge University Press:  06 October 2014

Christopher Sharpley
Christopher Sharpley*
Affiliation:
Monash University
*
Faculty of Education, Monash University, Clayton, Victoria 3168, Australia
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Abstract

The use of visual analysis alone to determine the presence of significant and generalizable effects in typical behavioural interventions is subject to a series of possible errors which result in high levels of unreliability when data are analysed in this way. The presence of autocorrelation in most behavioural data poses a serious threat to visual and traditional analysis of such data, a threat which can be avoided by use of the more appropriate interrupted time-series (TMS) statistics. Although previously suggested as reasons for not using TMS procedures, the issues of model-identification and number of data points required for TMS are discussed and shown to be invalid arguments against the use of TMS. A case is made for visual analysis of behavioural data as an appropriate procedure only under certain constrained clinical conditions.

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Article
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Behaviour Change , Volume 3 , Issue 1 , March 1986, pp. 26 - 33
Copyright
Copyright © The Author(s) 1986

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References

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