Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Faktoriālais pirms- un pēcpārbaudes eksperimentālais dizains× | ANOVA ar atkārtotiem mērījumiem× | |
|---|---|---|
| Nozare≠ | Eksperimentu plānošana | Statistika |
| Saime≠ | Process / pipeline | Hypothesis test |
| Izcelsmes gads≠ | 1963 (canonical formalization) | 1992 |
| Autors≠ | Codified by Donald T. Campbell and Julian C. Stanley | Girden (textbook treatment); Field (2013) |
| Tips≠ | True experimental design | Parametric within-subjects mean comparison |
| Pirmavots≠ | Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally. link ↗ | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| Citi nosaukumi | factorial pre-post design, factorial repeated-measures pretest-posttest design, multi-factor pretest-posttest design, FPPD | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| Saistītās≠ | 6 | 4 |
| Kopsavilkums≠ | 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. | Repeated-measures ANOVA is a parametric hypothesis test that compares three or more measurements taken from the same individuals — typically across time points or conditions — to decide whether their means differ. It extends one-way ANOVA to within-subjects designs, as treated in standard references such as Girden (1992) and Field (2013). |
| ScholarGateDatu kopa ↗ |
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