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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