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Table 2 Model 1. Results of the binary logistic regression

From: Administrative data analysis of student attrition in hungarian medical training

Variables

B

S.E

Wald

df

Sig

Exp(B)

Gendera

.244

.118

4.3

1

.038

1.277

Citizenshipb

.363

.32

1.285

1

.257

1.438

Passive semestersc

-2.245

.69

10.59

1

.001

.106

The pace of credit accumulationd

3.786

.231

268.054

1

 < 0.001

44.099

Form of financee

1.771

.319

30.862

1

 < 0.001

5.879

Constant

-2.205

.125

312.039

1

 < 0.001

.110

  1. Source: Higher Education Information System Hungary, Hungarian Educational Authority, own edition
  2. a0 – Female, 1 – male.
  3. b0-Hungarian, 1- Foreign
  4. c0-Less than 2,1-2 or more
  5. dWe coded the pace of credit accumulation by the number of credits gained by the student at the end of the fourth semester: 0 – The student gained at least 60% of the suggested number of credits specified by the institution (120 credits), 1 – The student gained less than 60% of the suggested number of credits.
  6. e0- State-financed,1 -Self-financed