The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to reflect women’s and men’s lives. In pursuing the 2030 Sustainability Development Goals (SDGs), the availability of high- quality gender-sensitive data has generated the current informative outburst. In the process, gender-sensitive data collection has departed from a mere dis- aggregation between men and women towards an unprecedented multifaceted informational spectrum. Methods for full exploiting gender-sensitive statis- tics, both standard and big data, though, faces some levels of criticality. The traditional descriptive linear combinations of a collection of simple indica- tors yields contradictory order results, whereas inference has so far privileged latent modelling only, holding several constraints. A novel statistical perspec- tive stems from recent developments of Multivariate Latent Markov Models (MLMMs), suitable to express a latent characteristic both in time and space. In addition to introducing covariates, on any measurement scales, not only in the structural model bul also the measurement one, MLMMs are innovative in so that they can handle a vast mass of data from very different sources. Thus they lead the way to an extensive investigation of the gender gap, account- ing for apparent contradictions in rankings and hence highlighting different paths, or “transition”, toward a more equitable society.

Crippa, F., Bertarelli, G., & Mecatti, F. (2021). A spatio-temporal approach to latent variables: modelling gender (I'm)balance in the Big Data era. In T. Rudas, & G. Peli (a cura di), Pathways Between Social Science and Computational Social Science (pp. 159-177). Springer [10.1007/978-3-030-54936-7_7].

A spatio-temporal approach to latent variables: modelling gender (I'm)balance in the Big Data era

Crippa F.
;
Bertarelli G.;Mecatti F.
2021

Abstract

The pursuance of gender equality has embraced a longstanding sta- tistical engendering process, to reflect women’s and men’s lives. In pursuing the 2030 Sustainability Development Goals (SDGs), the availability of high- quality gender-sensitive data has generated the current informative outburst. In the process, gender-sensitive data collection has departed from a mere dis- aggregation between men and women towards an unprecedented multifaceted informational spectrum. Methods for full exploiting gender-sensitive statis- tics, both standard and big data, though, faces some levels of criticality. The traditional descriptive linear combinations of a collection of simple indica- tors yields contradictory order results, whereas inference has so far privileged latent modelling only, holding several constraints. A novel statistical perspec- tive stems from recent developments of Multivariate Latent Markov Models (MLMMs), suitable to express a latent characteristic both in time and space. In addition to introducing covariates, on any measurement scales, not only in the structural model bul also the measurement one, MLMMs are innovative in so that they can handle a vast mass of data from very different sources. Thus they lead the way to an extensive investigation of the gender gap, account- ing for apparent contradictions in rankings and hence highlighting different paths, or “transition”, toward a more equitable society.
No
Scientifica
Capitolo o saggio
Gender balance, gender-sensitive, big data, computational social science;
English
Pathways Between Social Science and Computational Social Science
978-3-030-54935-0
Crippa, F., Bertarelli, G., & Mecatti, F. (2021). A spatio-temporal approach to latent variables: modelling gender (I'm)balance in the Big Data era. In T. Rudas, & G. Peli (a cura di), Pathways Between Social Science and Computational Social Science (pp. 159-177). Springer [10.1007/978-3-030-54936-7_7].
Crippa, F; Bertarelli, G; Mecatti, F
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/300779
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