Rescaling results of mixed nonlinear probability models to compare regression coefficients or variance components across hierarchically nested models
Because of the scaling of the unobserved latent dependent variable in logistic and probit multilevel models, the lowest level residual variance is always pi^2/3 (logistic regression) or 1.0 (probit regression). As a consequence, a change of regression coefficients and variance components between hierarchically nested models cannot be interpreted unambiguously. To overcome this issue, rescaling of the unobserved latent dependent variable of nested models to the scale of the intercept-only model has been proposed (Hox 2010). In this talk, we demonstrate the use of the program meresc, which implements this procedure to rescale the results of mixed nonlinear probability models such as xtmelogit, xtlogit, or xtprobit.
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