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Table 5 Parameter estimates θ’s of the segmented regression models by degree course

From: An analysis of Italian university students’ performance through segmented regression models: gender differences in STEM courses

Variable Bio Biot Chem
Intercept − 1.06* (0.35) 0.71 (0.46) − 3.63* (0.62)
Gender (female)    
Male − 0.59 (0.45) − 0.85 (0.58) 0.00 (0.67)
CU male 0.00 (0.02) 0.04* (0.01) 0.01 (0.02)
CU female − 0.02 (0.02) 0.02 (0.01) − 0.03 (0.02)
HSdiploma (other “liceo”)    
Scientific − 0.10 (0.11) − 0.68* (0.18) − 0.10 (0.29)
Classical − 0.14 (0.13) − 0.56* (0.2) − 0.10 (0.32)
Technical − 0.35* (0.18) − 0.72* (0.3) − 0.36 (0.35)
Vocational − 0.51* (0.23) − 0.32 (0.39) − 0.85 (0.68)
Abroad/other − 0.57 (0.46) − 0.29 (0.54) − 3.13* (1.19)
Age (≤19)    
> 19 − 0.41* (0.10) − 0.30 (0.16) − 0.74* (0.17)
Macro-region (Islands)    
North 0.36* (0.11) − 0.23 (0.20) 0.17 (0.20)
Center 0.20 (0.11) − 0.17 (0.20) 0.09 (0.21)
South − 0.25* (0.11) − 0.07 (0.20) − 0.64* (0.24)
HS final mark (60) 0.01 (0.00) − 0.01* (0.00) 0.03* (0.01)
HSdiploma x Gender (female)    
Scientific − 0.14 (0.36) − 0.24 (0.44) − 0.40 (0.63)
Classical − 0.29 (0.41) − 0.45 (0.49) − 1.14 (0.72)
Technical 0.30 (0.42) − 0.20 (0.53) − 0.65 (0.67)
Vocational 0.45 (0.48) − 0.54 (0.70) − 0.21 (0.94)
Abroad/other 0.51 (0.99) − 0.24 (1.18) 2.66 (1.44)
  1. Baselines are in brackets. Cohort of freshmen enrolled in 2014. Biology, biotechnology and chemistry. The asterisk indicates a corresponding p-value <0.05. Standard errors are in brackets