CSIE_analysis_ASE4
by Irimia Mihaela
2025-09-22
Academic profiles of admitted candidates
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.
Objective The analysis compares candidates admitted to state-funded and tuition-paying places in ASE master’s programs, highlighting differences in performance and score distributions. It aims to identify patterns of competitiveness, variability, and stratification among candidates across faculties, reflecting both academic excellence and demand for specific programs.
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 (tuition free and tuition fee)
Tuition free candidates
Bachelor score
The mean value of the bachelor score only for the admitted candidates - tuition free candidates
Admission test score
The mean value of the admission test score only for the admitted candidates - tuition free candidates
Final admission score
The mean value of the final admission score only for the admitted candidates - tuition free candidates
Tuition fee candidates
Bachelor score
The mean value of the bachelor score only for the admitted candidates - tuition fee candidates
Admission test score
The mean value of the admission test score only for the admitted candidates - tuition fee candidates
Final admission score
The mean value of the final admission score only for the admitted candidates - tuition fee candidates
Conclusions
We observe that the distributions of candidates admitted to state-funded places, based on their bachelor’s exam grades, are left-skewed and mostly leptokurtic. This indicates that the majority of these candidates achieved very high grades at the bachelor’s exam. Furthermore, there are no significant differences among candidates, as most grades are concentrated around the mean. In the case of the faculties of Public administration and management, Business and tourism, Accounting and management information systems, Agri-Food and environmental economics, and Finance, insurance, banking and stock exchanges, the distributions are platykurtic, which suggests greater variability/diversity among candidates and a more heterogeneous level of preparation. Therefore, there are notable differences between very well-prepared candidates and those less prepared, and the selection process becomes clearer, as differentiation among candidates is based on their exam grades.
For tuition free places, admission test distributions reveal two distinct patterns. Highly competitive programs (Business administration in foreign languages, International economic relations, Accounting, and Law) are characterized by higher scores and strong candidate homogeneity. In contrast, other programs (Public Administration, Business and Tourism, Finance, Management, and Marketing) show lower scores and greater variability, suggesting a less selective admission process.
In the case of candidates admitted to tuition-fee places, we observe that the admission averages are somewhat lower compared to those of candidates admitted to state-funded places, ranging from 6.32 (for candidates from the faculty of Agri-food and environmental economics) to 9.00 (for candidates from the faculty of Business administration in foreign languages). Most distributions are left-skewed, indicating that candidates with higher scores also predominate among those admitted to tuition-paying places. However, in faculties such as Cybernetics, statistics and economic informatics, and Agri-Food and environmental economics, candidates with below-average scores are more common.
The observed differences between candidates admitted to state-funded and tuition-paying places reflect a stratification based on academic performance. Nevertheless, in certain faculties, the performance gap between these two categories is relatively small, suggesting consistently high demand regardless of funding type. This highlights the importance of carefully balancing admission policies to ensure both equity and competitiveness across programs.
Also, in this case, 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