Autocorrelation and estimates of treatment effect size for singlecase experimental design data
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AbstractWe examined the degree of autocorrelation among singlecase design data with six measures used to estimate treatment effect size. The most commonly used measures of effect size for singlecase data over the last 5 years published in peerreviewed journals were selected for comparison. The overall mean degree of autocorrelation was 0.46 (SD=0.33) across the 304 data paths, which represents a moderate degree of autocorrelation. Overall, it appears that nonparametric measures of effect size (i.e., percent of nonoverlapping data [PND], nonoverlap of all pairs [NAP], and improvement rate difference [IRD] values) were substantially and significantly more influenced by the degree of autocorrelation. TauU effect size estimate was the nonparametric exception as it was not significantly influenced by the degree of autocorrelation. Parametric measures of effect sizes such as standardized mean difference (SMD) and log response ratio (LRR) values did not appear to be significantly influenced by the degree of autocorrelation. For SMD, LRR, and TauU values, the correlation between the effect size value and the degree of autocorrelation was minimal. For NAP, IRD, and PND values, the correlation between the effect size value and the degree of autocorrelation was moderate, indicating that these estimates of effect size should be avoided as the degree of autocorrelation between data points increases.
