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| 贝叶斯双因素方差分析× | 混合方差分析× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1961 (foundations); 2012 (default Bayes factor formulation) | 1925 |
| 提出者≠ | Harold Jeffreys (foundational); modern default-prior form by Jeffrey N. Rouder et al. | R. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentation |
| 类型≠ | Bayesian hypothesis test | Parametric factorial ANOVA |
| 开创性文献≠ | Rouder, J. N., Morey, R. D., Speckman, P. L., & Province, J. M. (2012). Default Bayes factors for ANOVA designs. Journal of Mathematical Psychology, 56(5), 356–374. DOI ↗ | Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE. ISBN: 978-1526419521 |
| 别名 | Bayesian factorial ANOVA, Bayes factor two-way ANOVA, Bayesian 2×k ANOVA, Bayesian two-factor ANOVA | split-plot ANOVA, mixed-design ANOVA, between-within ANOVA, Karma ANOVA (Mixed ANOVA — Gruplar Arası × Tekrarlı) |
| 相关≠ | 4 | 6 |
| 摘要≠ | Bayesian two-way ANOVA extends the classical two-way analysis of variance by replacing p-values with Bayes factors and posterior distributions. It quantifies evidence for or against main effects and their interaction using prior-weighted model comparison, yielding conclusions that are directly interpretable in probabilistic terms rather than relying on a fixed significance threshold. | Mixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in which different groups are each measured across multiple time points or conditions. |
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