Abstract

Women are underrepresented in growing positions such as those related to STEM field careers (i.e., science, technology, engineering, and mathematics). One of the causes for remaining out of that field could lie on gender stereotypes. Undergraduate stereotypes and beliefs are important as could easily uphold future gender segregation at the workplace. In the research arena the measurement of those biased beliefs is important as most commonly used Likert scales (LS) could raise problems in terms of accuracy. As fuzzy rating scales (FRS) are a promising measurement alternative, the aim of this study is to compare the properties of FRS against LS. We conducted a cross-sectional study with 262 STEM and non-STEM participants who answered to a questionnaire that, besides gendered beliefs and injustice perception towards the situation of women at the workplace, included personal characteristics as coursed degree and working experience. Results pointed out, on one hand, that FRS allowed for a better capture of the variability of individual responses, but on the other hand, that LS were better valued than FRS in what is concerned with satisfaction and ease of response. Advantages of FRS for psychosocial measurement are discussed to facilitate the study around causes of segregation that excludes women from the STEM labour market.

Highlights

  • In the present times, growing Industry 4.0 grounds on the information and communications technology (ICT), conditioning both the educational and the labour environments [1]

  • As for reliability indexes, they were quite similar for fuzzy rating scales (FRS) and Likert scales (LS), being slightly higher for FRS than for LS in the case of the belief in a just world

  • It can be said that the results obtained through the LS differed from those obtained through the FRS: statistically significant differences were found for the scores on the traditional conception of gender roles and the belief in a just world among participants with and without working experience that had not been revealed by LS analyses

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Summary

Introduction

In the present times, growing Industry 4.0 grounds on the information and communications technology (ICT), conditioning both the educational and the labour environments [1]. Gender discrimination could lead to important social costs as it contributes to the loss of the advantages of labour diversity [6], hindering equality expectations agenda in terms of Decent Work [7] and Sustainable Development Goals of the United Nations [8]. This would make those countries where gender inequalities in access to STEM careers occur, to be relegated in social and economic development in the future. Taking all of this into account, if we want to promote a sustainable labour, the measurement and the analysis of gendered beliefs that may imply the underrepresentation of women in STEM careers, are crucial and probably more necessary than ever

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