Single-subject and time-series designs have recently been advocated as a preferred method of examining clinical change in individual patients. Data from single-subject designs are frequently analyzed by means of graphic presentation and visual inspection. The presence of serial dependency or autocorrelation in data collected from a single individual can reduce the reliability and accuracy of visual inferences. Fifty-four data paths from single-subject research published in the occupational therapy literature were reviewed to determine the degree of serial dependency present in each data set. The results revealed that a large portion (41 %) of the data sets contained a significant degree of autocorrelation. The implications of a high degree of serial dependency in relation to data analysis and interpretation are discussed, and methods to reduce the effect of serial dependency are suggested.
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