  M-quantile random-effects regression represents an interesting approach for
modelling multilevel data when the interest of researchers is focused on the
conditional quantiles. When data are based on complex survey designs, sampling
weights have to be incorporate in the analysis. A pseudo-likelihood approach
for accommodating sampling weights in the M-quantile random-effects regression
is presented. The proposed methodology is applied to the Italian sample of the
"Program for International Student Assessment 2015" survey in order to study
the gender gap in mathematics at various quantiles of the conditional
distribution. Findings offer a possible explanation of the low share of females
in "Science, Technology, Engineering and Mathematics" sectors.
