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。
| 有条件过程分析（有调节的中介）× | 分层线性模型 (HLM / 多层模型)× | |
|---|---|---|
| 领域≠ | 因果推断 | 统计学 |
| 方法族≠ | Regression model | Hypothesis test |
| 起源年份≠ | 2018 | 1986 |
| 提出者≠ | Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation) | Raudenbush & Bryk (popularized); Goldstein (parallel development) |
| 类型≠ | Regression-based conditional process model | Parametric nested-data regression |
| 开创性文献≠ | Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654 | Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 |
| 别名≠ | moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model | HLM, MLM, multilevel modeling, multilevel analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals. | 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. |
| ScholarGate数据集 ↗ |
|
|