Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Kifani wa ABA× | Jaribio la Kiwango× | |
|---|---|---|
| Nyanja | Muundo wa Majaribio | Muundo wa Majaribio |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1968 (ABA base); factorial extensions developed through 1980s–2000s | 1926–1935 |
| Mwanzilishi≠ | Derived from ABA reversal design (Baer, Wolf & Risley, 1968) extended with factorial manipulation principles | Ronald A. Fisher |
| Aina≠ | Single-case experimental design with factorial treatment structure | Quantitative experimental design |
| Chanzo asilia≠ | Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association. ISBN: 978-1433807909 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Majina mbadala | Factorial reversal design, Multi-factor ABA design, Factorial withdrawal design, SCED factorial ABA | factorial design, factorial ANOVA design, multi-factor experiment, crossed-factor design |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | 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 experiment is an experimental design in which two or more independent variables (factors) are manipulated simultaneously, and every combination of their levels is tested. Introduced by Ronald Fisher in the 1920s–1930s, it is the standard approach whenever a researcher needs to detect not only the main effect of each factor but also whether the effect of one factor depends on the level of another — the interaction effect. |
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