2014
DOI: 10.1007/s40489-014-0037-2
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A Sensitivity Analysis of Three Nonparametric Treatment Effect Scores for Single-Case Research for Participants with Autism

Abstract: The widely used percentage of nonoverlapping data (PND) treatment effect calculation was compared to more recently developed methods which, it has been argued, better account for outlying variables and trend in single-case design (SCD) intervention studies. Percentage of all nonoverlapping data (PAND) and nonoverlap of all pairs (NAP) were selected for comparison as both are amenable to hand calculation, making them widely accessible to clinicians and teachers as well as researchers. A data set was developed t… Show more

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Cited by 4 publications
(4 citation statements)
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“…Nonparametric statistics have been developed for describing the difference between baseline and treatment conditions (i.e., effect sizes) for single‐subject design research, but have not been used to aid in the interpretation of functional analysis outcomes. For example, the percentage of nonoverlapping data (PND; Scruggs, Mastropieri, & Casto, ) is a conservative approach (Carr, ) that identifies the number of points during treatment that do not overlap with the highest baseline point and divides that sum of nonoverlapping points by the total number of treatment points to provide an effect size between 0 and 100%. Effect sizes calculated using simple statistics like PND have been found to be useful in treatment efficacy reviews (e.g., Campbell, ; Carr, Severtson, & Lepper, ; Heyvaert et al, ); however, it is unknown if PND effect sizes between the test and control conditions of a functional analysis would correspond to, or be more or less sensitive to, a structured criteria or visual analysis of functional analysis data.…”
mentioning
confidence: 99%
“…Nonparametric statistics have been developed for describing the difference between baseline and treatment conditions (i.e., effect sizes) for single‐subject design research, but have not been used to aid in the interpretation of functional analysis outcomes. For example, the percentage of nonoverlapping data (PND; Scruggs, Mastropieri, & Casto, ) is a conservative approach (Carr, ) that identifies the number of points during treatment that do not overlap with the highest baseline point and divides that sum of nonoverlapping points by the total number of treatment points to provide an effect size between 0 and 100%. Effect sizes calculated using simple statistics like PND have been found to be useful in treatment efficacy reviews (e.g., Campbell, ; Carr, Severtson, & Lepper, ; Heyvaert et al, ); however, it is unknown if PND effect sizes between the test and control conditions of a functional analysis would correspond to, or be more or less sensitive to, a structured criteria or visual analysis of functional analysis data.…”
mentioning
confidence: 99%
“…The type of data gathered for participants with ASD often includes few data points, particularly in baseline when challenging behaviours are present. As such, the comparison of three non‐parametric methods reported that PND was frequently applicable and, as suggested elsewhere in the literature the continued use of PND appeared justified (Carr, ; Scruggs & Mastropieri, ).…”
Section: Methodsmentioning
confidence: 74%
“…A recent comparison of three non‐parametric calculations compared treatment effect scores derived using PND, percentage of all non‐overlapping data (PAND), and non‐overlap of all pairs (NAP) calculation methods (Carr, ). The PND score is calculated by counting the number of treatment data points that exceed the most extreme baseline data point as determined by the expected direction of the increase or decrease in target behaviour.…”
Section: Methodsmentioning
confidence: 99%
“…Advantages of PND include ease of calculation from graphical rather than raw data, high degree of inter-rater reliability, applicability to any SCD design type, and ease of interpretation (Campbell, 2013 ; Parker et al, 2007 ). PND has also been shown to be a more conservative measure of treatment effects compared to other nonoverlap methods (e.g., nonoverlap of all pairs (NAP) and percentage of all overlapping data (PAND)) and is widely applicable to data gathered for participants with ASD (Carr, 2015 ). PND is calculated by dividing the number of treatment data points that fall below the lowest baseline data point by the total number of data points in the treatment phase, multiplied by 100 (Scruggs et al, 1987 ).…”
Section: Methodsmentioning
confidence: 99%