English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

The use of sampling weights in the M-quantile random-effects regression: an application to PISA mathematics scores