CSIE_analysis_ASE2
by Irimia Mihaela
2025-09-21
Academic profiles of 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.
The purpose of this analysis is to examine candidates’ academic performance in order to identify patterns, compare results across faculties, and highlight the factors that could influence competitiveness in ASE master’s programs. In this article, our purpose is to analyze the candidates’ admission results in ASE master’s programs across 12 faculties using the available data on ASE, master admission, Decision No. 89/14.05.2025 platforms, 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.
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
## Graphical and numerical analysis of numerical variables
Admission results distribution for each faculty
In Figure below we present the admission score, final admission score and bachelor score distributions for each analyzed faculty (the graph are made without excluding the rejected and absent candidates). Distribution of admission test score, bachelor score and final admission score by each faculty:
The mean value of the admission test score for all candidates
Admission test score
Bachelor score
Final admission score
Conclusions
We observe that the bachelor’s degree grades obtained by the candidates are high, ranging from 8.63 (at the faculty of Law) to 9.66 (achieved by candidates from the faculty of Business and tourism and from Theoretical and applied economics). Most candidates have above-average grades, with high values predominating, which suggests competition among candidates with similar performance levels. Furthermore, we notice that most grades are concentrated around the mean, with the exception of the faculty of Accounting and management information systems, where bachelor’s grades are more dispersed and uniform. 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.
A similar pattern is observed in the distribution of candidates according to the grades obtained in the admission exam, which follows the same trend as the previous ones. It can be seen that most candidates achieve high grades, while very few obtain low grades. The most competitive faculties appear to be: Business administration in foreign languages, International economic relations (median = 9.13, IQR = 1.29), Business and tourism (median = 8.80, IQR = 1.53), and Cybernetics, statistics and economic informatics (median = 8.29, IQR = 1.19).
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