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

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.