পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| তুলনামূলক মডেল পরীক্ষণ গবেষণা× | তুলনামূলক নিশ্চিতকরণ গবেষণা (Comparative Confirmatory Research)× | |
|---|---|---|
| ক্ষেত্র | গবেষণা নকশা | গবেষণা নকশা |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1969–2000s | 1971 (Jöreskog); systematized in organizational research by 2000 |
| প্রবর্তক≠ | Rooted in structural equation modeling traditions; formalized through Jöreskog (1969) and extended by Vandenberg & Lance (2000) | Karl Jöreskog (multigroup CFA foundation); Robert Vandenberg & Charles Lance (organizational application) |
| ধরন≠ | Quantitative confirmatory-comparative research design | Quantitative comparative research design |
| মৌলিক উৎস≠ | Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Press. ISBN: 978-1462523344 | Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3(1), 4–70. DOI ↗ |
| অপর নাম | comparative model comparison, cross-group model testing, competing model comparison research, comparative structural model evaluation | multigroup confirmatory research, cross-group confirmatory study, comparative hypothesis testing design, comparative model testing research |
| সম্পর্কিত | 4 | 4 |
| সারসংক্ষেপ≠ | Comparative model testing research is a quantitative design in which two or more theoretically motivated models — or the same model evaluated across distinct groups or conditions — are systematically tested and compared using fit indices, likelihood-ratio tests, or information criteria. The goal is to determine which model better represents the data structure, or whether a model's parameter structure holds equally across comparison groups. | Comparative confirmatory research tests whether a pre-specified theoretical model or set of hypotheses holds equivalently across two or more distinct groups, time points, or contexts. It extends standard confirmatory analysis by explicitly imposing and evaluating equality constraints across groups, determining not only whether a model fits the data but whether its structure, factor loadings, and parameter estimates are comparable across populations. This design is foundational to cross-cultural, multi-site, and subgroup comparison studies. |
| ScholarGateডেটাসেট ↗ |
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