1 / 10

STATISTICAL ANALYSIS OF UNIVERSITY RATINGS

STATISTICAL ANALYSIS OF UNIVERSITY RATINGS. Bakhrushin V.E. (2011), Osvita i Upravlinnia, № 1, P. 7 – 1 2. https://www.researchgate.net/publication/232727899_Statistical_analysis_of_University_rankings_(in_Ukrainian)/file/d912f50941e26e6bd3.pdf.

morag
Download Presentation

STATISTICAL ANALYSIS OF UNIVERSITY RATINGS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. STATISTICAL ANALYSIS OF UNIVERSITY RATINGS Bakhrushin V.E. (2011), Osvita i Upravlinnia, № 1, P. 7 – 12. https://www.researchgate.net/publication/232727899_Statistical_analysis_of_University_rankings_(in_Ukrainian)/file/d912f50941e26e6bd3.pdf

  2. Some a priori requirements for the statistical characteristics • Satisfactory resolving power: • mean value should be close to the middle of the interval of possible values; • skewness must be close to zero; • standard deviation should be in limits 0.13 – 0.25 from the difference between maximum and minimum scores, depending on the number of analyzed universities: • The absence of correlation between components.

  3. Empirical distribution functions (EDF) of rating overall scores (R/10) (R/10) score

  4. EDF calculation and properties F(R) = n/N, where n is an sequence number of the university in the sample ordered in ascending of R; N – is the total number of universities in rating. F(R) value is a probability that the value of the outcome rating score does not exceed R. We can see that the functions for the different ratings are very different from each other. In particular, for Ukrainian TOP-200 Universities we have highly skewed distribution, and for Times and National rating of the Russian universities – inhomogeneous distributions.

  5. Distribution models ARWU: Times: Russian: Ukrainian TOP-200: N(a;b) – normal distribution; L(a;b) – lognormal distribution with parameters a and b.

  6. Statistical properties of score distributions

  7. EDF for components of Sunday Times – 2007 rating

  8. Correlation of ARWU components For ARWU rating correlation coefficients between the components usually exceed 0.5, and in some cases may be up to 0.87.

  9. Correlation of the Ukrainian TOP-200 Universities components NTUU “KPI” T. Shevtchenko Kyiv Univ. Kharkov NU NTU “KhPI” Education quality National medical Univ. Staff quality For the rest pairs of component also there is a significant correlation

  10. Correlation of the Sunday Times-2007 components Job placement Staff quality For the rest pairs of component also, as a rule, there is no significant correlation

More Related