Linganisha mbinu

	Uchambuzi wa Neno-pamoja kwa Msingi wa Meta-Regression	Meta-Regression
Nyanja	Saintometriki	Meta-uchanganuzi
Familia	Process / pipeline	Regression model
Mwaka wa asili	2000s–2010s (hybrid application period)	2002
Mwanzilishi	Derived from Callon et al. (co-word analysis, 1983) and Glass (meta-regression lineage, 1976); hybrid application developed incrementally in scientometrics and evidence synthesis	Simon Thompson & Julian Higgins
Aina	Hybrid scientometric-statistical method	Weighted regression for effect-size heterogeneity
Chanzo asilia	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.	Thompson, S. G., & Higgins, J. P. T. (2002). How should meta-regression analyses be undertaken and interpreted? Statistics in Medicine, 21(11), 1559–1573.
Majina mbadala	MR-CWA, meta-regression co-word mapping, regression-weighted co-word analysis, co-word meta-regression	Meta-Analytic Regression, Weighted Regression in Meta-Analysis, Moderator Analysis, Meta-regresyon
Zinazohusiana	4	2
Muhtasari	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.	Meta-regression is a statistical technique that extends conventional meta-analysis by regressing study-level effect sizes on one or more study characteristics (moderators) to explain between-study heterogeneity. Formalized by Thompson and Higgins in 2002, it uses weighted least squares — weighting each study by the inverse of its variance — within a mixed-effects framework, allowing researchers to identify which study features systematically account for variation in observed effects across the literature.