方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 分层线性模型 (HLM / 多层模型)× | 单因素方差分析× | 重复测量方差分析× | |
|---|---|---|---|
| 领域 | 统计学 | 统计学 | 统计学 |
| 方法族 | Hypothesis test | Hypothesis test | Hypothesis test |
| 起源年份≠ | 1986 | 1925 | 1992 |
| 提出者≠ | Raudenbush & Bryk (popularized); Goldstein (parallel development) | Ronald A. Fisher | Girden (textbook treatment); Field (2013) |
| 类型≠ | Parametric nested-data regression | Parametric mean comparison | Parametric within-subjects mean comparison |
| 开创性文献≠ | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ | Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed., Ch. 14). SAGE. ISBN: 978-1446249185 |
| 别名≠ | HLM, MLM, multilevel modeling, multilevel analysis | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA | within-subjects ANOVA, repeated measures analysis of variance, rm-ANOVA, Tekrarlı Ölçüm ANOVA |
| 相关 | 4 | 4 | 4 |
| 摘要≠ | Hierarchical Linear Modeling (HLM), also known as Multilevel Modeling (MLM), is a parametric statistical method for analyzing nested or clustered data — for example students within classrooms, patients within hospitals, or employees within organizations. Formalized by Raudenbush and Bryk in their 2002 seminal text (building on work from the mid-1980s), HLM simultaneously estimates individual-level and group-level effects while correctly partitioning variance across levels. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. | 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). |
| ScholarGate数据集 ↗ |
|
|
|