Methoden vergleichen

	Meta-Regressions-basierte Co-Word-Analyse	Co-word-Analyse
Fachgebiet	Szientometrie	Szientometrie
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	2000s–2010s (hybrid application period)	1983
Urheber	Derived from Callon et al. (co-word analysis, 1983) and Glass (meta-regression lineage, 1976); hybrid application developed incrementally in scientometrics and evidence synthesis	Michel Callon, Jean-Pierre Courtial, and colleagues
Typ	Hybrid scientometric-statistical method	Scientometric network analysis technique
Wegweisende Quelle	Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.	Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.
Aliasnamen	MR-CWA, meta-regression co-word mapping, regression-weighted co-word analysis, co-word meta-regression	keyword co-occurrence analysis, co-word mapping, keyword co-word network, CWA
Verwandt	4	6
Zusammenfassung	Meta-regression-based co-word analysis is a hybrid scientometric technique that enriches traditional co-word mapping by weighting keyword co-occurrence networks with meta-regression-derived effect estimates. Instead of treating all documents as equally informative, the method uses statistical regression to incorporate study-level moderators — such as publication year, sample size, or methodological quality — into the co-occurrence structure, revealing how thematic clusters in a research field vary across moderator conditions.	Co-word analysis is a scientometric technique that quantifies how often pairs of keywords, subject terms, or title words appear together across a corpus of publications. By treating simultaneous occurrence as a proxy for conceptual relatedness, it constructs networks and clusters that reveal the intellectual structure, dominant themes, and emerging sub-fields of a research domain.