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Table 6 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 Comp Eng Math
Intercept − 1.97 (1.05) − 2.78* (0.23) − 3.24* (0.79)
Gender (female)    
Male − 4.32* (1.09) − 0.75* (0.30) − 0.64 (0.95)
CU male 0.01 (0.02) − 0.04* (0.01) − 0.07 (0.05)
CU female − 0.78* (0.39) − 0.02 (0.01) − 0.17* (0.05)
HSdiploma (other “liceo”)    
Scientific − 0.67 (0.39) − 0.01 (0.13) 0.36 (0.30)
Classical − 0.64 (0.54) − 0.09 (0.14) 0.51 (0.37)
Technical − 1.13* (0.4) − 0.30 (0.16) − 0.06 (0.44)
Vocational − 1.36 (0.75) − 1.06* (0.53) − 0.61 (1.01)
Abroad/other 0.89 (1.50) − 0.13 (0.36) − 24.93 (573.89)
Age (≤19)    
>19 − 0.49* (0.11) − 0.58* (0.06) − 1.32* (0.33)
Macro-region (Islands)    
North 1.40* (0.21) 0.10 (0.06) 0.55 (0.30)
Center 0.93* (0.23) − 0.07 (0.07) 0.56 (0.31)
South 0.34 (0.22) − 0.19* (0.07) − 0.02 (0.32)
HS final mark (60) 0.02* (0.00) 0.02* (0.00) 0.04* (0.01)
HSdiploma x Gender (female)    
Scientific 1.75* (0.60) 0.33 (0.24) − 0.79 (0.84)
Classical 2.17* (0.78) 0.42 (0.26) 0.17 (0.95)
Technical 1.92* (0.60) 0.53* (0.25) − 0.04 (0.93)
Vocational 1.90* (0.92) 1.18* (0.58) 0.46 (1.64)
Abroad/other − 0.38 (1.63) 0.32 (0.45) 1.58 (847.23)
  1. Baselines are in brackets. Cohort of freshmen enrolled in 2014. Computer Science, engineering and mathematics. The asterisk indicates a corresponding p-value <0.05. Standard errors are in brackets