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Journal of Population Sciences

Table 2 Parameter estimates θ’s of the segmented regression model in Eq. 4

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

Variable

Category

Estimate

S.E.

P value

Intercept

 

− 1.96

0.16

0.00

Gender f

Male

− 1.03

0.19

0.00

CU male

 

− 0.02

0.01

0.03

CU female

 

− 0.03

0.01

0.01

 

Scientific

− 0.15

0.06

0.02

 

Classical

− 0.17

0.07

0.02

HSdiploma (Other “liceo”)

Technical

− 0.39

0.08

0.00

 

Vocational

− 0.45

0.15

0.00

 

Abroad/Other

− 0.45

0.21

0.03

Age (≤ 19)

> 19

− 0.53

0.04

0.00

 

Biotechnology

− 0.26

0.06

0.00

 

Chemistry

0.03

0.06

0.69

 

Computer Science

− 0.49

0.06

0.00

Degree course (Biology)

Engineering

− 0.29

0.04

0.00

 

Mathematics

− 0.32

0.07

0.00

 

Natural Sciences

0.08

0.06

0.20

 

Physics

− 0.28

0.07

0.00

 

Statistics

0.12

0.10

0.23

 

North

0.24

0.04

0.00

Macro-region of enrolment (Islands)

Center

0.07

0.05

0.17

 

South

− 0.18

0.05

0.00

HS final mark (60)

 

0.02

0.00

0.00

 

Scientific

0.28

0.14

0.05

 

Classical

0.33

0.16

0.03

HSdiploma x Gender (Female)

Technical

0.39

0.15

0.01

 

Vocational

0.39

0.21

0.07

 

Abroad/other

0.50

0.28

0.08

  1. Baselines are in brackets. Cohort of freshmen enrolled in 2014