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Statistical Decision-Making Accuracies for Some Overlap- and Distance-based Measures for Single-Case Experimental Designs.


ABSTRACT: Selecting a quantitative measure to guide decision making in single-case experimental designs (SCEDs) is complicated. Many measures exist and all have been rightly criticized. The two general classes of measure are overlap-based (e.g., percentage nonoverlapping data) and distance-based (e.g., Cohen's d). We compare several measures from each category for Type I error rate and power across a range of designs using equal numbers of observations (i.e., 3-10) in each phase. Results showed that Tau and the distance-based measures (i.e., RD and g) provided the highest decision accuracies. Other overlap-based measures (e.g., PND, dual-criterion method) did not perform as well. It is recommended that Tau be used to guide decision making about the presence/absence of a treatment effect, and RD or g be used to quantify the magnitude of the treatment effect.

Supplementary information

The online version contains supplementary material available at 10.1007/s40614-021-00317-8.

SUBMITTER: Carlin MT 

PROVIDER: S-EPMC8894531 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Statistical Decision-Making Accuracies for Some Overlap- and Distance-based Measures for Single-Case Experimental Designs.

Carlin Michael T MT   Costello Mack S MS  

Perspectives on behavior science 20211122 1


Selecting a quantitative measure to guide decision making in single-case experimental designs (SCEDs) is complicated. Many measures exist and all have been rightly criticized. The two general classes of measure are overlap-based (e.g., percentage nonoverlapping data) and distance-based (e.g., Cohen's <i>d</i>). We compare several measures from each category for Type I error rate and power across a range of designs using equal numbers of observations (i.e., 3-10) in each phase. Results showed tha  ...[more]

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