Performance criteria-based effect size (PCES) measurement of single-case experimental designs: A real-world data study

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AYDIN O., Tanious R.

JOURNAL OF APPLIED BEHAVIOR ANALYSIS, vol.55, no.3, pp.891-918, 2022 (SSCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 55 Issue: 3
  • Publication Date: 2022
  • Doi Number: 10.1002/jaba.928
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, Periodicals Index Online, ABI/INFORM, Abstracts in Social Gerontology, Business Source Elite, Business Source Premier, Child Development & Adolescent Studies, CINAHL, Communication Abstracts, EBSCO Education Source, Education Abstracts, EMBASE, ERIC (Education Resources Information Center), MEDLINE, MLA - Modern Language Association Database, Psycinfo
  • Page Numbers: pp.891-918
  • Keywords: acceptable performance criteria, effect size, mastery criterion, nonoverlap based effect sizes, single-case experimental designs, social validity, AUTISM SPECTRUM DISORDER, QUANTITATIVE SYNTHESIS, SUBJECT RESEARCH, VISUAL ANALYSIS, INTERVENTION, STUDENTS, SKILLS, METAANALYSIS, DIFFERENCE, CHILDREN
  • Anadolu University Affiliated: No


Visual analysis and nonoverlap-based effect sizes are predominantly used in analyzing single case experimental designs (SCEDs). Although they are popular analytical methods for SCEDs, they have certain limitations. In this study, a new effect size calculation model for SCEDs, named performance criteria-based effect size (PCES), is proposed considering the limitations of 4 nonoverlap-based effect size measures, widely accepted in the literature and that blend well with visual analysis. In the field test of PCES, actual data from published studies were utilized, and the relations between PCES, visual analysis, and the 4 nonoverlap-based methods were examined. In determining the data to be used in the field test, 1,052 tiers (AB phases) were identified from 6 journals. The results revealed a weak or moderate relation between PCES and nonoverlap-based methods due to its focus on performance criteria. Although PCES has some weaknesses, it promises to eliminate the causes that may create issues in nonoverlap-based methods, using quantitative data to determine socially important changes in behavior and to complement visual analysis.