Biomedical subjects
Publications and source records attributed to R S Vaught.
This paper uses a question-and-answer format to present the technical aspects of interrupted time-series analysis (ITSA). Topics include the potential relevance of ITSA to behavioral researchers, serial dependency, time-series models, tests of significance, and sources of ITSA information.
Comparisons between visual and time-series inferences from behavioral data show that serial dependency in scores is likely to disrupt agreement between the two methods of analysis. If researchers follow an earlier recommendation that time-series analysis be used to supplement or confirm visual analysis, this study's findings suggest that the two methods will disagree most often when the data contain high levels of autocorrelation and when reliable behavorial changes are indicated by time-series analysis.
A time-series method is presented, nontechnically, for analysis of data generated in individual-subject operant studies, and is recommended as a supplement to visual analysis of behavior change in reversal or multiple-baseline experiments. The method can be used to identify three kinds of statistically significant behavior change: (a) changes in score levels from one experimental phase to another, (b) reliable upward or downward trends in scores, and (c) changes in trends between phases. The detection of, and reliance on, serial dependency (autocorrelation among temporally adjacent scores) in individual-subject behavioral scores is emphasized. Examples of published data from the operant literature are used to illustrate the time-series method.