THE EFFECT OF AUTOCORRELATION ON THE RESULTS OF VISUALLY ANALYZING DATA FROM SINGLE-SUBJECT DESIGNS

Objective. Single-subject research designs are used to conduct clinica l research and outcome evaluation in occupational therapy. Confusion e xists regarding the best method to analyze and interpret single-subjec t data. Method. One hundred graphs displaying the results of published single-subject research were examined to determine the influence of a utocorrelation on the visual inferences made by the original investiga tors. The graphs were selected fi om 20 articles published over 10 yea rs in seven rehabilitation journals. The data were extrapolated and la g 1 auto-correlation coefficients computed for both the baseline and t reatment phases. Results. Data analysis focused on two issues: (a) whe ther a relationship existed between the amount of autocorrelation pres ent in a graph and the conclusion on the basis of visual analysis and (b) whether the amount of autocorrelation varied across different phas es of the single-subject graphs. When a significant degree of autocorr elation was present, researchers using visual analysis were more likel y to conclude that there war no clinically significant change in perfo rmance. Autocorrelation values were significantly higher in the treatm ent phases of the single-subject designs. Conclusion. Additional resea rch is needed to establish a set of decision rules to assist clinician s in using visual analysis to evaluate the results of single-subject r esearch.