পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| শ্রেণিবদ্ধ রৈখিক মডেল (HLM)× | মাল্টিলেভেল মডেলিং× | |
|---|---|---|
| ক্ষেত্র≠ | পরিসংখ্যান | গবেষণা পরিসংখ্যান |
| পরিবার≠ | Regression model | Process / pipeline |
| উদ্ভবের বছর | 1992 | 1992 |
| প্রবর্তক≠ | Bryk & Raudenbush | Anthony Bryk and Stephen Raudenbush |
| ধরন≠ | Multilevel linear regression | Method |
| মৌলিক উৎস≠ | Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2nd ed.). Sage Publications. ISBN: 978-0761919049 | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| অপর নাম | HLM, multilevel linear model, nested data model, random coefficient model | HLM, mixed-effects models, random effects models, MLM |
| সম্পর্কিত≠ | 4 | 3 |
| সারসংক্ষেপ≠ | The Hierarchical Linear Model (HLM) is a multilevel regression method designed for data in which lower-level units (e.g., students, patients) are nested within higher-level groups (e.g., schools, hospitals). It simultaneously models within-group relationships and between-group variation, producing unbiased estimates and correct standard errors that ordinary regression cannot provide for nested data. | 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. |
| ScholarGateডেটাসেট ↗ |
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