Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño experimental factorial pretest-posttest× | Diseño experimental de parcelas divididas× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia≠ | Process / pipeline | Hypothesis test |
| Año de origen≠ | 1963 (canonical formalization) | 1935 |
| Autor original≠ | Codified by Donald T. Campbell and Julian C. Stanley | Frank Yates |
| Tipo≠ | True experimental design | Parametric mixed-model ANOVA |
| Fuente seminal≠ | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ | Yates, F. (1935). Complex Experiments. Supplement to the Journal of the Royal Statistical Society, 2(2), 181–247. DOI ↗ |
| Alias≠ | factorial pre-post design, factorial repeated-measures pretest-posttest design, multi-factor pretest-posttest design, FPPD | split-plot ANOVA, whole-plot sub-plot design, Bölünmüş Parsel Deseni (Split-Plot) |
| Relacionados | 6 | 6 |
| Resumen≠ | A factorial pretest-posttest experimental design combines the simultaneous manipulation of two or more independent variables (factors) with measurement of the dependent variable both before and after treatment. This structure allows researchers to assess the main effect of each factor, all possible interaction effects between factors, and the magnitude of change from pretest to posttest — all within a single, fully randomised experiment. | The split-plot design is a parametric experimental design that applies one factor to large whole plots and a second factor to subdivisions (sub-plots) within each whole plot. It was introduced by Frank Yates in 1935 to handle agricultural experiments where one factor — such as irrigation or tillage method — is difficult or impractical to change frequently, while a second factor can be varied more easily within the same plot. |
| ScholarGateConjunto de datos ↗ |
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