Interpreting treatment effect size from single case experimental design data: a preliminary analysis of differential effects of treatments designed to increase or decrease behaviour
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AbstractBackgroundEstimates of treatment effect size from single case experimental design (SCED) data may be impacted by the direction for treatment effects (i.e. ascending or descending slope for the dependent variable). Estimating effect sizes for treatments designed to decrease behaviour are potentially more restricted because the intended direction for treatment is zero (i.e. an absolute basal). Conversely, effect sizes for interventions that increase behaviour are less restricted due to a relatively unconstrained ceiling from a pure measurement standpoint (i.e. no absolute ceiling). That is, treatments that increase behaviour have a broader range of possible effect size values as the ceiling is only limited by demand characteristics and the learners' skills and motivation to exhibit the behaviour.MethodThe current study represents a preliminary analysis of the mean and range of SCED effect sizes for treatments designed to either increase or decrease target behaviour. A withincase Cohen's d measure that was developed for SCED data was used to estimate treatment effect sizes.ResultsResults indicated that the mean and range of effect size values for treatments that increased behaviour were significantly greater compared with treatments that decreased behaviour.ConclusionsResults are discussed in terms of developing standards, or best practices, specific to interpreting effect size values and meeting quality control requirements for inclusion of the data set in future SCED metaanalytic studies estimating treatment effect size. Specifically, preliminary results suggest that benchmarks for low, medium and high SCED effect size values need to be developed separately for treatments that increase or decrease levels of the dependent variable.
