At Egon Zehnder we've been measuring executive potential for more than 30 years, and we've identified the predictors that correlate strongly with competence at the top.
Although use of a standard random intercepts model results in a biased estimate for the effect of the predictor that is correlated with the random effect, Bouwmeester et al. [ 3] explained that, in fact, this correlation is beneficial in terms of prediction and particularly calibration, because inclusion of that predictor partly explains differences in prevalence between clusters.
This study combined surface currents, wind velocity, sea surface temperature (SST), UV radiation, photosynthetically active radiation (PAR), and chlorophyll-a concentration for the western Indian Ocean and known relationships with coral bleaching and mortality to derive predictor variables that correlate with thermal stress.
In a second step, a multicollinearity analysis was computed with all significant bivariate predictors to eliminate the confounding of predictors that were correlated with each other (correlation coefficient ≥ .50).50
Moreover, when multiple predictors were highly correlated with one another (ρ > 0.85), we selected the predictor that was most strongly correlated with the exposure, although we preferentially excluded latitude and longitude and selected raster-based land use data (collected in 2000) over older aerial photography-based data (collected in the 1970s and 1980s).
To explore a link between MFH and Ras, we utilized previously described oncogenic pathway predictors that correlate with RAS activity [13].
This study examined the relation between correctness of estimation and several data characteristics: number of events per variable (EPV), number of predictors, percentage of predictors that are highly correlated, percentage of predictors that were non-null, size of regression coefficients, and size of correlations.
In statistics, when there are more than two predictors that are highly correlated, it is called collinearity [1, 2].
In the multilevel analysis, only those variables will be considered as predictors that are significantly correlated to the physiological measures on the basis of 10-s measurement intervals.
