Comparing "Visual'' Effect Size Indices for Single-Case Designs

Effect size indices are indispensable for carrying out meta-analyses and can also be seen as an alternative for making decisions about the effectiveness of a treatment in an individual applied study. The desirable features of the procedures for quantifying the magnitude of intervention effect include educational/clinical meaningfulness, calculus easiness, insensitivity to autocorrelation, low false alarm, and low miss rates. Three effect size indices related to visual analysis are compared according to the aforementioned criteria. The comparison is made by means of data sets with known parameters: degree of serial dependence, presence or absence of general trend, and changes in level and/or in slope. The percent of nonoverlapping data showed the highest discrimination between data sets with and without intervention effect. In cases when autocorrelation or trend is present, the percentage of data points exceeding the median may be a better option to quantify the effectiveness of a psychological treatment.