Methoden vergleichen
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| PRISMA-basierter Review mit bibliometrix-Unterstützung× | Systematische Literaturübersicht× | |
|---|---|---|
| Fachgebiet | Szientometrie | Szientometrie |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2017 (bibliometrix); 2009/2021 (PRISMA) | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Urheber≠ | Aria & Cuccurullo (bibliometrix package); Moher et al. / Page et al. (PRISMA statement) | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| Typ≠ | Software-assisted systematic review workflow | Evidence synthesis methodology |
| Wegweisende Quelle≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| Aliasnamen | bibliometrix PRISMA review, R-bibliometrix systematic review, bibliometrix-enhanced evidence synthesis, bibliometrix-supported PRISMA review | SLR, systematic review, evidence synthesis review, structured literature review |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | A bibliometrix-assisted PRISMA-based review combines the structured, transparent reporting framework of PRISMA with the quantitative science-mapping capabilities of the bibliometrix R package. The approach embeds bibliometric analyses — such as citation analysis, co-authorship mapping, and keyword co-occurrence — into the evidence-synthesis steps of a PRISMA-guided systematic review, enabling both rigorous literature screening and macro-level visualization of the intellectual landscape. | 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. |
| ScholarGateDatensatz ↗ |
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