مقایسهٔ روشها
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| پژوهش تأییدی سلسلهمراتبی× | مدلسازی چندسطحی× | |
|---|---|---|
| حوزه≠ | طراحی پژوهش | آمار پژوهش |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 1980s–2000s | 1992 |
| پدیدآور≠ | Raudenbush & Bryk; Hox; Goldstein | Anthony Bryk and Stephen Raudenbush |
| نوع≠ | Quantitative confirmatory research design | Method |
| منبع بنیادین≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage. ISBN: 978-0761919049 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| نامهای دیگر | multilevel confirmatory research, nested confirmatory design, hierarchical hypothesis-testing research, HCR | HLM, mixed-effects models, random effects models, MLM |
| مرتبط≠ | 5 | 3 |
| خلاصه≠ | Hierarchical confirmatory research is a quantitative design that tests pre-specified hypotheses about relationships or group differences in data that have a natural nested (hierarchical) structure — such as students clustered within classrooms, patients within hospitals, or employees within organizations. By explicitly modeling the hierarchy, it avoids the inflation of Type I error that occurs when nested data are analyzed as though observations were independent. | 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. |
| ScholarGateمجموعهداده ↗ |
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