Comparar métodos

	Desenho Fatorial ABA	Delineamento Experimental Fatorial de Sujeito Único
Área	Delineamento experimental	Delineamento experimental
Família	Process / pipeline	Process / pipeline
Ano de origem	1968 (ABA base); factorial extensions developed through 1980s–2000s	1970s–1980s
Autor original	Derived from ABA reversal design (Baer, Wolf & Risley, 1968) extended with factorial manipulation principles	Applied behavior analysis tradition; systematized in Barlow & Hersen (1984) and Kazdin (1982)
Tipo	Single-case experimental design with factorial treatment structure	Experimental single-subject design with multiple independent variables
Fonte seminal	Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association. ISBN: 978-1433807909	Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881
Outros nomes	Factorial reversal design, Multi-factor ABA design, Factorial withdrawal design, SCED factorial ABA	factorial SCED, factorial single-case design, factorial N-of-1 design, factorial within-subject experimental design
Relacionados	6	6
Resumo	The Factorial ABA design embeds a factorial treatment structure within the ABA reversal framework. Rather than testing a single treatment against baseline, the researcher systematically varies two or more independent variables (factors) across treatment phases, using the ABA withdrawal logic to establish experimental control. This makes it possible to examine main effects and interactions among treatment components within a single-case or small-N experimental context.	A factorial single-subject experimental design applies the logic of factorial experiments — manipulating two or more independent variables simultaneously to study main effects and interactions — within a single-subject (N=1 or small N) repeated-measures framework. Instead of comparing groups, the same individual serves as their own control across systematically varied conditions, enabling fine-grained analysis of how multiple treatment components combine to influence behavior or clinical outcomes.