Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Meta-regresná metaanalýza× | Systematický prehľad literatúry× | |
|---|---|---|
| Odbor | Scientometria | Scientometria |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1993–1999 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Tvorca≠ | 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) |
| Typ≠ | Quantitative evidence synthesis with covariate modeling | Evidence synthesis methodology |
| Pôvodný zdroj≠ | 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 ↗ |
| Ďalšie názvy | meta-regression, meta-analytic regression, weighted regression meta-analysis, MR-MA | SLR, systematic review, evidence synthesis review, structured literature review |
| Príbuzné≠ | 4 | 5 |
| Zhrnutie≠ | 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. |
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