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With the random intercepts accounted for in the calculation of the regression coefficients, the results report the population-wide effect of the predictor variables on the dependent variable (Hamilton 2013, p. 394).
As in Experiment 1, for the detection task, we also ran two repeated measures linear regression GEE models to explore the effect of the predictor variables on signal-detection estimates d' and c.
To explore the effect of the predictor variables across sub-sets of the population (e.g., continents) we conducted a series of random-intercept-and-slope models allowing the slopes associated with each given SCP predictor variable to vary across continents.
For the detection task, we ran two additional repeated measures linear regression GEE models to explore the effect of the predictor variables on signal detection estimates d' and c.
The level 1 coefficient is the within-person effect of the predictor and the level 2 coefficient is the between-person effect.
Scaling effect estimates is necessary to obtain total effect estimates that represent the total effect of the predictor on the outcome, conditional on the mediation.
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A more comprehensive picture of the effect of the predictors on the response variable can be obtained by using quantile regression.
This model removes the effect of those time-invariant characteristics, so it can be assessed the net effect of the predictors on the outcome variable.
We analysed the effect of the predictors reported in 1998 on sickness absence reported in 2000.
A sample size of 60 subjects was estimated to provide > 70% to detect a moderate to large effect of the predictors of interest.
Far out most of the participants indeed choose hospital A and, as a result, we could not investigate the effect of the predictors on the hospital choice.
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