Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Meta-analyse met tijdsneden× | Meta-regressiegebaseerde meta-analyse× | |
|---|---|---|
| Vakgebied | Scientometrie | Scientometrie |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 1992 (cumulative form); refined through 2000s | 1993–1999 |
| Grondlegger≠ | Lau et al. (cumulative variant); Borenstein et al. (general meta-analytic framework) | Stephen G. Thompson & Simon J. Sharp (systematic framework); earlier work by Berlin, Longnecker & Greenland (1993) |
| Type≠ | Quantitative evidence synthesis variant | Quantitative evidence synthesis with covariate modeling |
| Oorspronkelijke bron≠ | Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to Meta-Analysis. Wiley. ISBN: 978-0470057247 | Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: a comparison of methods. Statistics in Medicine, 18(20), 2693–2708. DOI ↗ |
| Aliassen | temporal meta-analysis, period-stratified meta-analysis, time-segmented meta-analysis, chronological meta-analysis | meta-regression, meta-analytic regression, weighted regression meta-analysis, MR-MA |
| Verwant≠ | 5 | 4 |
| Samenvatting≠ | Time-sliced meta-analysis is a variant of standard meta-analysis in which the primary studies are partitioned into successive time periods (slices) and a separate pooled effect estimate is computed for each period. By comparing pooled effects across periods, researchers can detect whether an intervention's effectiveness, a relationship's magnitude, or a methodological consensus has shifted over time. This temporal lens transforms a static evidence summary into a longitudinal narrative of how scientific knowledge on a topic has evolved. | 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. |
| ScholarGateGegevensset ↗ |
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