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Teaching Visual Analysis of Time Series Data

Published online by Cambridge University Press:  16 June 2009

Ian A. James,
Paul S. Smith and
Derek Milne
Ian A. James
Affiliation:
Newcastle City Health NHS Trust
Paul S. Smith
Affiliation:
Northumberland Clinical Psychology Service
Derek Milne
Affiliation:
University of Newcastle upon Tyne
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Extract

Visual analysis, or “eyeballing”, of single subject (N=l) data is the commonest technique for analysing time series data. The present study examined firstly, psychologists' abilities to determine significant change between baseline (A) and therapeutic (B) phases, and secondly, the decision making process in relation to the visual components of such graphs. Thirdly, it looked at the effect that a training programme had on psychologists' abilities to identify significant A−B change. The results revealed that the participants were poor at identifying significant effects from non-significant changes. In particular, the study found a high rate of false alarms (Type 1 errors), and a low rate of misses (Type 2 errors), i.e. high sensitivity but poor specificity. The only visual components to significantly alter decisions were the degree of serial dependency and the mean shift component. The teaching influenced the participants' judgements. In general, participants became more conservative, but there was limited evidence of a significant improvement in their judgements following the teaching.

Information

Type
Research Article
Information
Behavioural and Cognitive Psychotherapy , Volume 24 , Issue 3 , July 1996, pp. 247 - 261
Copyright
Copyright © British Association for Behavioural and Cognitive Psychotherapies 1996

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