Could someone please explain to me the concept behind why adding additional covariates to explore if they are moderators of the effect is actually making R^2 ( amount of between-study heterogeneity explained, decrease? My ill-advised brain is telling me that even if they are not significant moderators, the extra information contained in the variable should add something to the picture, and remove some of the heterogeneity?
Meta regression: adding covariates is decreasing amount of heterogeneity explained - how?
- Thread starter tcpsych7
- Start date
The adjusted R^2 allows for the number of predictors in a way that reduces R^2 as the predictors goes up. At the same time raw R^2 is increasing, so it is a race between the two effects. if the new predictor is important the increase dominates. If it isn't then the reduction effect wins. So with insignificant predictors the raw R^2 goes up slightly but the value is adjusted down even more.
The adjusted R^2 allows for the number of predictors in a way that reduces R^2 as the predictors goes up. At the same time raw R^2 is increasing, so it is a race between the two effects. if the new predictor is important the increase dominates. If it isn't then the reduction effect wins. So with insignificant predictors the raw R^2 goes up slightly but the value is adjusted down even more.