CSIE_analysis_ASE3
by Irimia Mihaela
2025-09-22
Academic profiles of admitted candidates: from bachelor’s GPA to admission results
Warning: The dataset analyzed in this study is not an official list provided by ASE and was created based on the information available on site.
This study examines the admission outcomes of candidates applying to ASE master’s programs across 12 faculties, namely:
Facultatea de Business și turism (BT)/Faculty of Business and tourism
Facultatea de Contabilitate și informatică de gestiune (CIG)/Faculty of Accounting and management information systems
Facultatea de Cibernetică, statistică și informatică economică (CSIE)/Faculty of Cybernetics, statistics and economic informatics
Facultatea de Drept (DA) /Faculty of Law
Facultatea de Economie teoretică și aplicată (ETA)/Faculty of Theoretical and applied economics
Facultatea de Finanțe, Asigurări, Bănci și Burse de Valori (FABBV)/Faculty of Finance, insurance, banking and stock exchange
Facultatea de administrarea afacerilor cu predare în limbi străine (FABIZ)/Faculty of Business administration in foreign languages
Facultatea de Management (MAN)/Faculty of Management
Facultatea de Marketing (MRK)/Faculty of Marketing
Facultatea de relații economice internaționale (REI)/Faculty of International business and economics.
By analyzing the available data on ASE, master admission, Decision No. 89/14.05.2025 platforms, we aim to identify patterns of academic performance, compare results across faculties, and explore potential factors that influences the competitiveness among applicants.
The analysed variables are presented below:
## Rows: 4,392
## Columns: 16
## $ nr_crt <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
## $ cod_candidat <dbl> 4397, 1068, 1726, 1670, 1841, 2369, 2372, 1681, 1078, …
## $ proba_scrisa <dbl> NA, 84, 84, 81, 78, 78, 78, 84, 75, 75, 75, 75, 75, 72…
## $ medie_concurs <dbl> NA, 9.76, 9.76, 9.64, 9.52, 9.52, 9.52, 9.46, 9.40, 9.…
## $ medie_licenta <dbl> 9.00, 10.00, 10.00, 10.00, 10.00, 10.00, 10.00, 9.50, …
## $ statut <chr> "Standard", "Standard", "Standard", "Standard", "Stand…
## $ rezultat <chr> "Absent", "Admis", "Admis", "Admis", "Admis", "Admis",…
## $ forma_invataman <chr> "Absent", "Buget", "Buget", "Buget", "Buget", "Buget",…
## $ specializare <chr> "EAM1", "EAM1", "EAM1", "EAM1", "EAM1", "EAM1", "EAM1"…
## $ program <chr> "Economia si administrarea afacerilor agroalimentare, …
## $ facultatea <chr> "Economie Agroalimentara si a Mediului", "Economie Agr…
## $ pob <dbl> 187, 187, 187, 187, 187, 187, 187, 187, 187, 187, 187,…
## $ pot <dbl> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, …
## $ nlb <dbl> 182, 182, 182, 182, 182, 182, 182, 182, 182, 182, 182,…
## $ nlt <dbl> 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36…
## $ ntl <dbl> 218, 218, 218, 218, 218, 218, 218, 218, 218, 218, 218,…Our dataset contains 16 variables — 10 numeric and 6 character — and 5,357 observations.
Main indicators of descriptive statistic
We want to check for duplicates and verify whether a candidate was admitted to more than one program:
The candidate with code 1000 applied to two programs (CSIE4 and CSIE3), but was admitted to only one of them (CSIE4) and so on.
We extract the first option with the biggest final admitted score of each candidate, but in some cases not admitted
## nr_crt cod_candidat proba_scrisa medie_concurs
## Min. : 1.0 Min. :1000 Min. : 0.00 Min. : 4.960
## 1st Qu.: 50.0 1st Qu.:2108 1st Qu.:51.00 1st Qu.: 7.750
## Median :116.0 Median :3002 Median :63.00 Median : 8.440
## Mean :154.2 Mean :2993 Mean :61.88 Mean : 8.365
## 3rd Qu.:249.0 3rd Qu.:3930 3rd Qu.:75.00 3rd Qu.: 9.070
## Max. :489.0 Max. :4827 Max. :90.00 Max. :10.000
## NA's :327 NA's :327
## medie_licenta statut rezultat forma_invataman
## Min. : 6.000 Diaspora : 73 Absent : 327 Absent : 327
## 1st Qu.: 8.660 Dizabilitati : 9 Admis :3369 Buget :1886
## Median : 9.330 Minoritati : 1 Respins: 696 Respins: 697
## Mean : 9.166 ProtectieSociala: 3 Taxa :1482
## 3rd Qu.:10.000 Rom : 3
## Max. :10.000 Standard :4303
##
## specializare program
## Length:4392 Length:4392
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## facultatea pob
## Cibernetica, Statistica si Informatica Economica :1017 Min. : 0.0
## Relatii Economice Internationale : 458 1st Qu.: 72.0
## Administrarea Afacerilor cu predare in limbi straine: 439 Median :187.0
## Marketing : 413 Mean :199.4
## Management : 406 3rd Qu.:339.0
## Contabilitate si Informatica de Gestiune : 402 Max. :361.0
## (Other) :1257
## pot nlb nlt ntl
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Min. : 29.0
## 1st Qu.: 1.00 1st Qu.: 39.0 1st Qu.: 32.0 1st Qu.: 54.0
## Median : 4.00 Median :106.0 Median : 75.0 Median :218.0
## Mean :12.16 Mean :105.5 Mean :116.7 Mean :222.2
## 3rd Qu.:12.00 3rd Qu.:123.0 3rd Qu.:226.0 3rd Qu.:328.0
## Max. :68.00 Max. :246.0 Max. :409.0 Max. :567.0
## Distribution of admission scores among accepted candidates
In Figure below we present the admission score, final admission score and bachelor score distributions for each analyzed faculty (the graph are made excluding the rejected and absent candidates).
Bachelor score
The mean value of the bachelor score only for the admitted candidates
Admission test score
The mean value of the bachelor score only for the admitted candidates
Final admission score
The mean value of the bachelor score only for the admitted candidates
Conclusions
The average bachelor’s exam grades of admitted candidates range from 9.72 (faculty of Theoretical and Applied Economics) to 8.53 (Faculty of Law). The highest grades are obtained by candidates from the faculty of Theoretical and applied economics (9.72), followed by those from the faculty of Cybernetics, statistics and economic informatics (9.6), and the faculty of Business and tourism (9.46). The distribution trends of bachelor’s grades are similar to those observed in the overall candidate population (the number of applicants registered at each faculty). Most distributions are leptokurtic and negatively skewed, suggesting that the majority of candidates achieve high grades in this exam. The distribution of candidates from the faculty of Accounting and management information systems is closer to a symmetric distribution, indicating greater diversity among candidates; however, even in this case, high grades still predominate.The differences between faculties are most likely influenced by the difficulty of the bachelor’s exams, the prestige and attractiveness of the master’s program, and the degree of homogeneity among the candidates who choose to pursue a specific program.
We observe that most distributions do not change significantly, and the general trend remains the same: candidates with high grades predominate, while the number of those with lower grades is smaller. In this context, the bachelor’s exam grades are concentrated around the median value, whereas the admission exam results (i.e., admission averages) are more dispersed, with a larger interquartile range and greater variability in candidates’ scores. The most competitive faculties continue to be: International economic relations , Business administration in foreign languages , Business and tourism , Theoretical and applied economics , and Cybernetics, statistics and economic informatics .
Therefore, the differentiation between candidates is made through the admission test and/or the specialized interview in which candidates are required to participate during the exam process.
References
- T. Pals, Research Finds that High School GPAs Are Stronger Predictors of College Graduation than ACT Scores, https://www.aera.net/Newsroom/Research-Finds-that-High-School-GPAs-Are-Stronger-Predictors-of-College-Graduation-than-ACT-Scores?utm_source=chatgpt.com, 2020.
- https://research.collegeboard.org/media/pdf/SAT_Score_Relationships_with_College_Degree_Completion.pdf?utm_source=chatgpt.com, accesat la 23.08.2025.
- https://ase.ro/comunicare/noutati/regulamentul-admitere-masterat-2025-2026/, accessed on 04.08.2025.
- https://ase.ro/admitere/masterat/, accessed on 28.07.2025.
- Decision No. 89/14.05.2025, accessed on 04.08.2025