विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| मेटा-रिग्रेशन-आधारित मेटा-विश्लेषण× | व्यवस्थित साहित्य समीक्षा× | |
|---|---|---|
| क्षेत्र | विज्ञानमिति | विज्ञानमिति |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1993–1999 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| प्रवर्तक≠ | Stephen G. Thompson & Simon J. Sharp (systematic framework); earlier work by Berlin, Longnecker & Greenland (1993) | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| प्रकार≠ | Quantitative evidence synthesis with covariate modeling | Evidence synthesis methodology |
| मौलिक स्रोत≠ | Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18(20), 2693–2708. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| उपनाम | meta-regression, meta-analytic regression, weighted regression meta-analysis, MR-MA | SLR, systematic review, evidence synthesis review, structured literature review |
| संबंधित≠ | 4 | 5 |
| सारांश≠ | Meta-regression-based meta-analysis extends standard meta-analysis by fitting a weighted regression model in which study-level characteristics (moderators) predict observed effect sizes. Rather than simply pooling effects, this approach asks why effects vary across studies — linking heterogeneity in outcomes to differences in population, intervention, design, or measurement features. It is the primary tool for explaining between-study variance in quantitative evidence synthesis. | A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic. |
| ScholarGateडेटासेट ↗ |
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