পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| বহুস্তরীয় নিশ্চিতি মূলক কার্যকারণ বিশ্লেষণ (MCFA)× | মাল্টিলেভেল মডেলিং× | |
|---|---|---|
| ক্ষেত্র≠ | মনোমিতি | গবেষণা পরিসংখ্যান |
| পরিবার≠ | Latent structure | Process / pipeline |
| উদ্ভবের বছর≠ | 1994 | 1992 |
| প্রবর্তক≠ | Bengt O. Muthen | Anthony Bryk and Stephen Raudenbush |
| ধরন≠ | Latent variable model / measurement model | Method |
| মৌলিক উৎস≠ | Muthen, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. DOI ↗ | Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Publications. DOI ↗ |
| অপর নাম | MCFA, multilevel measurement model, two-level CFA, hierarchical CFA | HLM, mixed-effects models, random effects models, MLM |
| সম্পর্কিত≠ | 6 | 3 |
| সারসংক্ষেপ≠ | Multilevel confirmatory factor analysis tests a pre-specified factor structure while simultaneously accounting for the non-independence of observations caused by clustered data. It decomposes item variance into within-group and between-group components, fitting a separate measurement model at each level, making it the standard tool for validating psychometric scales administered within natural groups such as classrooms, clinics, or organisations. | 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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