The statistical analysis of single-subject data: A comparative examination

Background and Purpose. The purposes of this study were to examine whether the use of three different statistical methods for analyzing single-subject data led to similar results and to identify components of graphed data that influence agreement (or disagreement) among the statistical procedures. Methods. Forty-two graphs containing single-subject data were examined. Twenty-one were AB charts of hypothetical data. The other 21 graphs appeared in Journal of Applied Behavioral Analysis, Physical Therapy, Journal of the Association for Persons With Severe Handicaps, and Journal of Behavior Therapy and Experimental Psychiatry. Three different statistical tests-the C statistic, the two-standard deviation band method, and the split-middle method of trend estimation-were used to analyze the 42 graphs. Results. A relatively low degree of agreement (38%) was found among the three statistical tests. The highest rate of agreement for any two statistical procedures (71%) was found for the two-standard deviation band method and the C statistic. A logistic regression analysis revealed that overlap in single- subject graphed data was the best predictor of disagreement among the three statistical tests (β=.49, P<.03). Conclusion and Discussion. The results indicate that interpretation of data from single-subject research designs is directly influenced by the method of data analysis selected. Variation exists across both visual and statistical methods of data reduction. The advantages and disadvantages of statistical and visual analysis are described.