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.

Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test anxiety and school performance by studying how anxiety affects the performance of students along the overall conditional distribution of mathematics, literature and science scores. We aim to indirectly measure whether higher goals increase test anxiety, starting from the hypothesis that high-skilled students generally set themselves high goals. We use an M-quantile regression approach that allows us to take into account the hierarchical structure and sampling weights of the PISA data. There is evidence of a negative and statistically significant relationship between test anxiety and school performance. The size of the estimated association is greater at the upper tail of the distribution of each score than at the lower tail. Therefore, our results suggest that high-performing students are more affected than low-performing students by emotional reactions to tests and school-work anxiety.

Measuring differences in the economic standard of living of between natives and other ethnic groups can inform us about the relative disadvantages and inequalities within Italian society. Despite the importance of this question, the measurement of this gap is not an easy task because, when using the usual design-based approach to survey sampling inference, the available micro-data lack sufficient sample size for the majority of immigrant communities needed to obtain reliable estimates. In this paper, we show that small area estimation (SAE) techniques can be applied in a fruitful way to avoid this issue. In particular, we use an approach based on M-quantile regression for estimating the economic standard of living in each community in Italy. Our findings highlight economic disparities between natives and other ethnic groups and suggest the need to adopt specific policies that target the most vulnerable immigrant communities and are designed to improve their economic standard of living

We analyze the PISA 2015 data for Italy using an M-quantile multilevel approach. This papers offers a complete overview of the relationship between test anxiety and school performance by studying how anxiety affects the performance of students along the overall conditional distribution of mathematics, literature and science scores.

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