方法对比
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| 多层模型× | 项目评估× | |
|---|---|---|
| 领域≠ | 研究统计学 | 田野方法 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1992 | 1960s–1970s (Scriven 1967; Stufflebeam CIPP model 1971) |
| 提出者≠ | Anthony Bryk and Stephen Raudenbush | Michael Scriven; Daniel Stufflebeam; Peter Rossi |
| 类型≠ | Method | Applied evaluation methodology |
| 开创性文献≠ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ | Rossi, P. H., Lipsey, M. W., & Freeman, H. E. (2004). Evaluation: A Systematic Approach (7th ed.). Sage. ISBN: 978-0761908944 |
| 别名 | HLM, mixed-effects models, random effects models, MLM | evaluation research, program assessment, educational evaluation, systematic program evaluation |
| 相关 | 3 | 3 |
| 摘要≠ | Multilevel modeling (also called hierarchical linear modeling, mixed-effects modeling) is a statistical framework for analyzing data organized in nested or clustered structures—students within schools, patients within hospitals, repeated measures within individuals. Developed by Bryk and Raudenbush (1992), it accounts for dependency among observations and partitions variance into levels (within-cluster and between-cluster), enabling valid inference and revealing context effects. Essential in education, medicine, organizational research, and any field where data have natural hierarchies. | Program evaluation is a systematic, empirically grounded process of collecting and analyzing information about a program to determine its merit, worth, or significance. Applied across education, public health, social services, and policy, it addresses questions such as whether a program is reaching its target population, whether it is being implemented as designed, and whether it is producing the intended outcomes. It draws on both quantitative and qualitative methods and serves accountability, improvement, or knowledge-generation purposes. |
| ScholarGate数据集 ↗ |
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