=Paper=
{{Paper
|id=Vol-2919/paper18
|storemode=property
|title=Multidimensional Statistical Analysis Methods in the Study of University Activities
|pdfUrl=https://ceur-ws.org/Vol-2919/paper18.pdf
|volume=Vol-2919
|authors=Andrey Mikryukov,Mikhail Mazurov,Dmitry Korneev,Vasilii Trembach
}}
==Multidimensional Statistical Analysis Methods in the Study of University Activities==
https://ceur-ws.org/Vol-2919/paper18.pdf
Multidimensional Statistical Analysis Methods in the Study of
University Activities
Andrey Mikryukov [0000-0002-8206-677X], Mikhail Mazurov [0000-0001-9993-4687],
Dmitry Korneev [0000-0001-7260-4768], Vasilii Trembach [0000-0001-7499-4368]
Plekhanov Russian University of Еconomics, Stremyanny lane 36, 117997 Moscow, Russia
Mikrukov.aa@rea.ru, Mazurov37@mail.ru
Korneev.dg@rea.ru, Trembach@yandex.ru
Abstract. The task of increasing the university's rating in international rating
systems is urgent. An approach to solving the problem is proposed to ensure the
required values of the basic indicators of the university's activity in the
international institutional ranking QS using models developed on the basis of
SWOT analysis methods, as well as correlation-regression and factor analysis.
SWOT - analysis methods allowed to identify a set of factors that affect the
main indicators of the university. Based on the methods of correlation-
regression analysis, estimates of the relationship between base indicators and
rating are obtained. A comparative analysis of the results obtained for the
universities of the reference group is carried out. Based on the methods of
factor analysis, a set of latent factors has been identified that have a significant
impact on the basic indicators. It is shown that measures to achieve the
specified indicators must be carried out considering the identified correlations
between latent factors and basic indicators, as well as the results of
interpretation of the developed factor model. The novelty of the developed
proposals lies in the assessment of the significance of latent factors influencing
the basic indicators of the university's activity, based on the use of correlation-
regression methods and methods of factor analysis. The developed factorial
model made it possible to structure and group the obtained data, as well as to
reduce the dimension of the problem being solved. The results obtained made it
possible to substantiate the conditions for achieving the required indicators of
the university ranking in the international institutional ranking QS.
Keywords: Сorrelation - Regression Analysis, Factor Analysis, Basic
Indicators, Institutional Rating.
1. Introduction
The Ministry of Education and Science has launched Project 5-100, which is a state
program to support the largest Russian universities [1]. The goal of the project is to
increase the prestige of Russian higher education and to bring at least five universities
out of the project participants to the top 100 universities in three authoritative world
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0).
Proceedings of the of the XXIII International Conference "Enterprise Engineering and Knowledge Management"
(EEKM 2020), Moscow, Russia, December 8-9, 2020.
rankings: Quacquarelli Symonds (QS), Times Higher Education (THE) and Academic
Ranking of World Universities.
Currently, the QS institutional ranking includes 25 Russian universities [2,3]. In
the first place among Russian universities is the Moscow State University, who
entered the top 100 of the institutional rating at 74th position, in second place -
Novosibirsk National Research State University (228th position), participating in the
"Project 5-100", in third place - St. Petersburg State University (225th position). The
Higher School of Economics (HSE), as of 2020, takes the 298th position, the National
Research Technological University "MISiS" - 428th position, the Plekhanov Russian
University of Еconomics - 755th position.
Over the past 5 years, Russian universities have shown noticeable dynamics in
entering the top 500 of the QS institutional rating, having increased their
representation by one and a half times, mainly due to the participants of the "Project
5-100". This indicator is one of the main benchmarks of the federal project "Young
Professionals".
In view of the above, the leadership of the Plekhanov Russian University of
Еconomics, the task was formulated to move in the world ranking of universities QS
by 2025. to the position currently occupied by MISIS. For this purpose, an analysis of
the conditions for achieving a given position was carried out and, based on the
developed models, proposals were justified to ensure the fulfillment of the task.
The purpose of the research is to develop scientifically grounded proposals for
increasing the target performance indicators of the university, considering the impact
on them of latent factors in the international institutional ranking QS to the required
values. The degree of achievement of the set values of the baseline readings and, as a
consequence, the rating of the university depends on changes in latent factors.
To achieve this goal, the methods of SWOT - analysis, as well as methods of
correlation - regression and factor analysis were used, which made it possible to
identify the degree of influence of latent factors on the basic indicators and the main
indicator (rating) functional.
The task was solved in 4 stages. At the first stage, the analysis of the correlation of
the basic indicators that ensure the promotion of Plekhanov Russian University of
Еconomics in the institutional ranking QS World University Ranking: academic
reputation, reputation with the employer, the ratio of the number of students to the
number of academic staff, citations per teacher, international teachers, international
students. The listed indicators are used in the university ranking system and are
presented in the QS - analytics information and analytical system [4]. Based on the
methods of correlation and regression analysis in the environment of the analytical
platform Deductor 5.3, the pairwise correlation coefficients of the functional values
and basic indicators for the Plekhanov Russian University of Еconomics and MISIS
University. Based on the obtained values, the analysis of the correlation of indicators
providing the promotion of Plekhanov Russian University of Еconomics in the QS
World University Ranking. The calculation results made it possible to assess the
closeness of the relationship between the base indicators and the rating functionality.
At the second stage, to identify the factors affecting the basic indicators of the
university's activity, to identify the most significant factors, their SWOT analysis was
carried out [5-8].
As you know, the SWOT analysis technique involves a deep analysis of the object
of research, provides the most objective assessment of it in terms of the strengths
(positive) and weak (negative) sides of the external and internal environment, as well
as opportunities and threats. The results of the SWOT analysis made it possible to
subsequently build the problem field of the situation, on the basis of which the goals
and objectives of cognitive modeling were formulated, and the structure of the
cognitive map was determined, with the help of which the problem of predicting the
performance indicators of the university was solved.
When constructing the problem field of the situation for structuring knowledge, the
object-structural approach was used [9], according to which the analysis and
presentation of knowledge is carried out in strategic, organizational, conceptual,
functional, spatial, temporal, causal and economic aspects (strata).
The purpose of a SWOT analysis is to maximize the strengths of professional
activities, minimize weaknesses, and use favorable opportunities to improve
activities. SWOT analysis includes an analysis of the situation inside the university,
as well as an analysis of external and internal factors and the situation in the
educational services market.
SWOT analysis made it possible to identify and structure the strengths and
weaknesses, potential opportunities and threats, as well as many factors, which must
be taken into account when developing a university development strategy and
achieving the required values of key indicators.
The SWOT analysis technology includes the following stages:
1. Formation of a list of strengths and weaknesses;
2. Formation of a list of risks (dangers) and opportunities;
3. Revealing connections between various elements of the lists;
4. Positioning of different strategy options.
For the SWOT analysis, materials related to the activities of the university were
used, as well as materials from the site of the International Institutional Ranking QS
World University Rankings [4].
The use of the SWOT analysis methodology provided a deep diagnosis of the
university's activities based on the totality of its assessments in the following areas:
- S (strength) - strengths;
- W (weakness) - weaknesses;
- O (opportunity) - favorable opportunities;
- T (threat) - threats.
The results of the analysis made it possible to structure the knowledge of experts
using the problem field of knowledge, to build and identify, based on the method of
expert assessments, a set of factors and the degree of their influence on the
performance indicators of the university.
Registration of the SWOT analysis results was carried out in a tabular form, where
the main elements were recorded according to the categories presented (Table 1).
If necessary, combinations of different elements of the SWOT analysis allow you
to form certain local strategies:
Table 1. Results of SWOT analysis.
The analyzed factors (characteristics) The degree of The degree
embodiment of the of
factor (characteristics) importance
Strong factor + of the factor
Weak factor - (characterist
ics)
1. Availability of well-known scientific Strong factor + 0,6
schools and dissertation councils
2. The presence of close collaboration with Strong factor + 0,3
foreign universities and research
organizations (the number of joint research
projects
3. Availability of basic departments at Strong factor + 0,2
enterprises
….. …. …..
N. Factor (characteristic) …. ….
1. The combination of "opportunities - strengths" - development strategy.
2. The combination of "opportunities - weaknesses" - a strategy for internal
transformation.
3. The combination of "threats - weaknesses" is seen as a limitation of strategic
development.
4. The combination of "threats - strengths" is used as a strategy for potential
benefits.
The result of the SWOT analysis was the identification and grouping of a set of
latent factors that affect the performance of the university. Since the number of
factors influencing the activities of the university is a significant value, it became
necessary to highlight the most significant factors, taking into account the correlation
relationships, including the factors of the second level that affect the factors of the
first level.
At the third stage of the study, the most significant latent factors affecting the basic
indicators of the university's activity were identified using the methods of factor
analysis. Their grouping was carried out, as well as an assessment of their
significance and the degree of influence on the basic indicators. The use of the
mathematical apparatus of factor analysis made it possible to reduce the dimension of
the problem being solved and to ensure the structuring of the data obtained.
Interpretation of the results of factor analysis made it possible to identify latent factors
that provide the main contribution to obtaining the result.
At the fourth stage of the study, a set of measures was substantiated to achieve
planned indicators to increase the institutional ranking of the QS University.
Thus, a new approach to solving the problem of providing conditions for achieving
the required values of the university performance indicators in the international
institutional ranking QS using models developed based on statistical analysis methods
is proposed. The novelty of the approach is determined by obtaining estimates of the
strength of the relationship between the basic indicators and their relationship with the
rating functional based on the methods of correlation-regression analysis, solving the
problem of identifying latent factors based on the application of SWOT-analysis
methods and the method of the main components of the developed factor model,
reducing the dimension of the problem being solved, since a large number of
interrelated (dependent, correlated) variables significantly complicates the analysis
and interpretation of the results obtained, and a reasonable assessment of the degree
of influence of latent factors on the basic indicators, which made it possible to
formulate a list of necessary measures to solve the problem of increasing the
university's rating.
Section 2 contains a literature review on the research topic, section 3 presents the
results of assessing the correlation of university performance indicators, section 4
based on the factor analysis method provides identification of latent factors and an
assessment of their significance, section 5 substantiates measures to achieve the
planned performance indicators of the university.
2. Literature Review
The issues of SWOT - analysis and its application for the study of socio-economic
systems are considered in works [5-8]. A fairly large number of works by domestic
and foreign scientists are devoted to the problem of applying the methods of
correlation-regression and factor analysis [10-28]. In works [10-16] theoretical issues
of statistical analysis are considered, in works [17-23] the features of the application
of methods of correlation-regression and factor analysis in the socio-economic sphere
are considered, in works [24-28] the features of building applied statistical models are
considered.
The analysis of the sources showed that in the presented formulation, the problem
of substantiating the conditions for achieving the required values of the university
performance indicators in the international institutional ranking QS using models
developed on the basis of methods of correlation-regression and factor analysis was
not solved.
3. Methodology. Application of correlation and regression analysis
of university performance indicators
The following initial data on the university for the period 2013 - 2020 were taken as a
basis for calculations: rating functionality, basic indicators - academic reputation;
reputation with the employer; the ratio of the number of students to the number of
teaching staff; citations per teacher; international teachers; international students.
Based on the methods of correlation and regression analysis in the environment of the
analytical platform Deductor 5.3, the pairwise correlation coefficients of the
functional values and basic indicators for the Plekhanov Russian University of
Еconomics, and for "MISiS" (Table 2) using the Pearson test (allows you to assess the
significance of differences between the actual and theoretical number of
characteristics of the sample).
Coefficients of pairwise correlation between the basic indicators were calculated
in a similar way. In accordance with the Chaddock scale (Table 3), an assessment of
the tightness of the connections of the correlation comparisons was carried out [10].
Table 2. Matrix of rating functional correlation with basic indicators using Pearson criteria.
Basic indicators Rating functional, Rating functional,
Plekhanov Russian MISIS
University of Еconomics
AR 0,152 0,854
ER 0,726 0,607
RS/T 0,939 0,511
CT 0,141 0,883
IT 0,182 0,494
IS 0,604 0,667
Table 3. Chaddock scale.
Pairwise correlation coefficient Bond strength
up to 0,3 Practically absent
0,3-0,5 Weak
0,5-0,7 Noticeable
0,7-0,9 Strong
The calculations made it possible to draw the following conclusions.
The presence of a strong connection between the rating functional and the basic
indicators was revealed: “The ratio of the number of students to the number of
teaching staff” (r = 0.939), “Reputation with employers” (r = 0.726) and
“International students” (r = 0.604). The strength of the link between the rating
functionality and other indicators is practically absent.
For the rating functional "MISiS" the greatest closeness of connection was revealed
for the basic indicators "Academic reputation" (0.854) and "Citations per teacher"
(0.883), the smallest - for the indicator "International teachers".
A more reliable criterion for assessing the tightness of relationships is a statistical
assessment of the coefficients of pair correlation by comparing its absolute value with
the table value rcrit, which is selected from a special table [11]. If the inequality ⎪rcalc
≥ rcrit⎪ is satisfied, then with a given degree of probability (usually 95%) it can be
argued that there is a significant linear relationship between the numerical populations
under consideration. That is, the hypothesis about the significance of the linear
relationship is not rejected. In the case of the opposite relation, i.e., for ⎪rcalc < rcrit⎪,
a conclusion is made about the absence of a significant connection.
In accordance with the table “Critical values of the correlation τcrit for the
significance level α = 0.05, the probability of an admissible error in the forecast 0.95,
and the degree of freedom f = n-k = 4 (for a given number of measurements n = 6, the
number of calculated constants k = 2, in the formula for calculating r involves two
constants ⎯x and ⎯ y), the tabular value rcrit = 0.811 is found.
The calculation results (hypothesis testing) are presented in table. 4 practically
confirmed the grades obtained on the Chaddock scale, except for the indicator
"International students".
Table 4. Strength of connection between functionality and indicators.
R= r2 rcalc rcrit Bond strength
AR 0,140 0,811 Insignificant
IT 0,194 0,811 Insignificant
IS 0,636 0,811 Insignificant
RS/T 0,952 0,811 Significant
ER 0,854 0,811 Significant
CT 0,174 0,811 Insignificant
The calculation of the coefficients of determination (R = r2), which is a measure of
the variability of the result y (the value of the rating functional) as a percentage of the
change in the factor (base indicator) x showed that for the base indicator "Number of
students in relation to the teacher" r2 = 0.9063 = 90.3% means that 90.3% of the
functional variation is determined by the basic indicator “Number of students in
relation to the teacher”.
At the next stage of the study, the identification and interpretation of latent factors
affecting the baseline indicators was carried out using the methods of factor analysis,
which is a class of multivariate statistical analysis procedures aimed at identifying
latent variables (factors) responsible for the presence of correlations between the
observed variables [12-14 ].
4. Results. Identification of Latent Factors and Assessment of their
Significance
Factors are groups of certain variables that correlate with each other more than with
the variables included in another factor. Thus, the meaningful meaning of the factors
can be identified by examining the correlation matrix of the initial data.
To assess the influence of latent factors on basic indicators, one of the most
common methods of factor analysis, the principal component method, was used,
which makes it possible to reduce a large number of interrelated variables, since a
large number of variables significantly complicates the analysis and interpretation of
the results [14].
The mathematical model of factor analysis is a set of linear equations in which
each observed variable xi is expressed as a linear combination of common factors F1,
F2, …, Fn and a unique factor Ui [14]:
where xi is a variable, i = 1, m, (m is the number of variables); n is the number of
factors; n˂m, aik - factor load; Fk - common factor, k = 1, n; Ui is a private factor.
The factor analysis procedure includes the following stages [15-18].
Stage 1. Construction of the correlation matrix of the system of variables by
calculating the Pearson's linear correlation coefficients.
Stage 2. Extracting factors and calculating factor loads aik, which are the main
subject of interpretation. At this stage, methods of component analysis (principal
component analysis), principal factors and maximum likelihood are used. When
solving the problem, the method of principal components was used, which made it
possible to select groups of closely correlated variables in a multidimensional space
and replace them with principal components without loss of information content.
The mathematical model of the principal component method is represented by
formula (3).
,
where: yj is the main component; αij is the coefficient reflecting the contribution of the
variable zi to the principal component yi; zi - standardized initial variable zi= (xi −
i)/si, si - variance, i = 1, k.
The calculation of the principal components is reduced to the calculation of
eigenvectors and eigenvalues (λ1, λ2, …, λk) of the correlation matrix of the initial data.
The αij values are factor loadings. They represent the correlation coefficients between
the original variables and the principal components. Factors include those variables
for which |αij| ˃ 0,7.
To reduce the dimension of the space Y = (y1, y2,…, yk) by cutting off non-
informative variables, the Kaiser criterion is used, which is associated with
eigenvalues: the number of principal components includes variables that correspond
to the eigenvalues λi ˃ 1, since their informative value is higher.
Stage 3. Rotation of the factorial solution, which is used if the selected factors
cannot be interpreted clearly enough.
For the analysis and interpretation of the results obtained, the varimax method and
the quartimax method are used [19-23]. Varimax is the method most often used in
practice, the purpose of which is to minimize the number of variables that have high
loads on the given factor (which helps to simplify the description of the factor by
grouping around it only those variables that are more associated with it than with the
rest ), cannot be used, since in the problem being solved the variables (basic
indicators) cannot be reduced, since they are all significant. Considering the above, to
interpret the results of factor analysis, we used quartimax, a method that ensures the
reduction (minimization) of the number of factors necessary to explain the variation
of a variable.
The mathematical apparatus of factor analysis made it possible to solve the
following two problems [24-28]:
1) reducing the dimension of the number of variables used due to their explanation
by a smaller number of factors;
2) grouping and structuring of the received data.
Thus, the result of the application of the principal component method is the
calculation of the eigenvalues of the factors, the volume of the explained variance
in% (the contribution of each factor to the obtained result), the total percentage of
variance (the total contribution of factors to the final result (Table 5) and the
construction of the matrix of factor loads (Table 6), which is the correlation
coefficients between the original variables (baseline indicators) and the main
components (factors).
Table 5. Factor analysis results.
Principal Eigenvalues Contribution to the Total contribution
components result
Value 1 3,715 61,9133% 61,9133%
Value 2 1,364 22,7306% 84,6439%
Value 3 0,585 09,7306% 94,3924%
Value 4 0,276 04,5992% 98,9916%
Value 5 0,060 01,0067% 99,9983%
Value 6 0.000 00.0017% 100,0000
The factor loadings matrix illustrates the strength of the relationship between a
variable and a factor. The higher the factor load in absolute terms, the higher the bond
strength.
The eigenvalue of the factor λi reflects its contribution to the variance of variables,
explained by the influence of general factors. In accordance with the Kaiser criterion,
it is believed that those factors for which this indicator is significantly less than 1.0 do
not make a significant contribution to the result explanation.
Table 6. Factor loading matrix.
Final factors (Quartimax method)
Variables
Factor 1 Factor2 Factor3 Factor4
AR 0,2466 0,684
ER 0,2938 0,8769 0,1026 0,3664
RS/T 0,1357 0,9455 0,1416 0.2490
CT 0,9754 0.1113 0,1470
IT 0,9906 0,1090
IS 0,7938 0,5435 0,2365
The second calculated indicator in Table 5 is the percentage of explained variance of
variables (column - total contribution). It is generally accepted that with a well-
grounded factorial solution, so many factors are chosen so that they together explain
at least 70-75% of the variance. In some cases, this figure can reach 85-90%.
In the problem being solved, the first 4 factors turned out to be significant (see
Table 7), providing a contribution to obtaining the result equal to 99%. The
contribution of the first factor is equal to 61.91%; the second factor - 22.73%; the
third factor is 9.75%, and the fourth factor is 4.60%.
The factorization of the matrix (the procedure for extracting factors) for various
levels of significance is carried out. It is generally accepted that with a well-grounded
factorial solution, so many factors are chosen so that they together explain at least 70-
75% of the variance. In some cases, this figure can reach 85-90%. The factor loadings
matrix illustrates the strength of the relationship between a variable and a factor. The
higher the factor load in absolute terms, the higher the bond strength.
Thus, the interpretation of the results of the performed factor analysis made it
possible to extract the significant factors of the first level (affecting the basic
indicators), the second level (affecting the factors of the first level) and calculate the
factor load.
The results of the interpretation of factor analysis and the identification of
significant factors of the first and second levels (with an indication of the expert
assessment of the factor's weight) made it possible to form a list of factors of the first
and second levels that affect the basic indicators:
Level 1 factors (affect the underlying factors): F1, The presence of well-known
scientific schools and dissertation councils (0.6); F2, Close collaboration with foreign
universities and research organizations (number of joint research projects (0.3); F3,
Availability of basic departments at enterprises (0.2); F4, Number of publications in
the Scopus database, WoS (0.6); F5, Availability of demanded directions and training
profiles (0.3); F6, The qualification level of the teaching staff (the number of the
teaching staff of the highest qualification) (0.2); F7, Number of teaching staff (0.6);
F8, The level of training (competencies) of students (0.5); F9, Number of teaching
staff with language training; (0.4); F10, places in a hostel (0.2); F11, Demand for
graduates from employers ((0.3); F12, Areas for educational activities (0.3); F13, The
level of payment for the teaching staff (0.4); F14, Stimulating factors (0.2); F20,
Foreign Entry Company (0.3).
Level 2 factors (affect the level 1 factors): F15, Expansion of the teacher social
package (0.3); F16, Change in the structure of employment of the teaching staff (0.3);
F17, The share of teaching staff planning to build an international scientific career
(0.2); F18, Academic mobility of the teaching staff (0.3); F19, Convergence of
educational programs with foreign universities (0.4); F21, Increase in the number of
On-line courses MOOCs (0.3), F22, Implementation of individual educational
trajectories (0.4); F23, Implementation of distance technologies (0.3); F24, The
tightness of the relationship with the employer (0.4).
The interrelationships of factors are presented in the form of a graph (Fig. 1), based
on which a cognitive model was subsequently developed.
When constructing a graph, the following designations are adopted:
AR - Academic reputation; ER - Reputation with employers; RS/T - The ratio of
the number of students to the number of teaching staff; CT - Citations per teacher; IT
- International teachers; IS - International students, F1-F24 factors of the first and
second orders.
In accordance with the results of the identification of factors, the most significant
factors influencing the baseline indicators are:
- factor 1, which affects the indicators of RR, CT, IT, IS and includes a set of
private factors: the number of teaching staff, the level of their qualifications and the
presence of close collaboration (the number of joint research projects) with foreign
universities and research organizations, foreign applicant company.
- factor 2, which affects the RS/T indicator and includes a combination of private
factors: the number of teaching staff, the level of payment for the teaching staff.
- factor 3, which affects the AR indicator and includes a set of private factors: The
presence of well-known scientific schools and dissertation councils, The presence of
Fig. 1. Relationships Graph between factors of the first and second levels F1-F24, basic
indicators (AR, ER, RS/T, CT, IT, IS) and the rating indicator F / R
close collaboration (the number of joint scientific projects) with foreign universities
and scientific organizations, The number of teaching staff, The level of their
qualifications, The introduction of distance technologies, The introduction individual
educational trajectories.
- factor 4, which affects the ER indicator and includes a set of particular factors: the
level of training (competencies) of students, the demand for graduates from the
employer, the presence of basic departments at enterprises, the introduction of
distance technologies, close interaction with the employer.
Thus, the results of the interpretation showed that the factors 1,2,3 have the greatest
influence on the basic indicators. Those, the task of increasing the values of these
indicators is directly related to the increase in the values of the factors of the first and
second levels, characterizing:
- The number of teaching staff and the level of their qualifications; The presence of
close collaboration (number of joint research projects) with foreign universities and
scientific organizations; The level of training (competencies) of students, The demand
for graduates from the employer; Availability of basic departments at enterprises;
Availability of well-known scientific schools and dissertation councils; Introduction
of remote technologies; Close interaction with the employer; Foreign entrant
company; Implementation of individual educational trajectories.
Based on the graph of relationships (Fig. 1), a cognitive model of scenario
forecasting has been developed, which makes it possible to select the most preferable
scenario for the increment of latent factors to achieve the required value of the rating
indicator under the conditions of the given restrictions.
5. Discussion. Measures to achieve the planned indicators of the
university
The results obtained in paragraphs 3-4 made it possible to substantiate a set of
measures to increase the values of particular indicators (factors) necessary to solve the
problem of achieving university performance indicators by 2025, corresponding to the
level of MISIS indicators in 2019.
Correlations between the functional and basic indicators are obtained. The
presence of a strong connection of the functional with the indicators: "The ratio of the
number of students to the number of teaching staff" (r = 0.952), "Reputation with
employers" (r = 0.854) and "International students" (r = 0.636) The strength of the
relationship of the functional with other basic indicators is insignificant.
The largest contribution (98.9%) to the final result (the value of the rating
functional and the corresponding place in the QS rating) is made by the following
particular indicators: The number of teaching staff and the level of their
qualifications; Close collaboration (number of joint research projects) with foreign
universities and research organizations; The level of training (competencies) of
students, The demand for graduates from the employer, The presence of basic
departments at enterprises, The presence of well-known scientific schools and
dissertation councils, The introduction of distance technologies; Close interaction
with the employer; Foreign entrant company; Implementation of individual
educational trajectories.
Measures to increase the values of latent indicators should be carried out
considering the obtained correlation dependences of the most significant factors
affecting the basic indicators.
6. Conclusion
The use of SWOT analysis methods made it possible to solve the problem of
identifying latent factors that affect the basic indicators of the university's activity. An
approach based on the methods of correlation-regression and factor analysis has been
developed to solve the problem of providing conditions for achieving the required
values of the performance indicators of the university in the international institutional
ranking QS
The developed correlation-regression model made it possible to calculate the
pairwise correlation coefficients of the values of the functional and basic indicators
for the Plekhanov Russian University of Еconomics and MISIS University, the rating
indicators of which are taken as a basis, as well as to carry out a comparative analysis
of the results obtained for the universities of the reference group, to reveal the
strength of the relationships between the basic indicators and their links with the
rating functionality.
The procedures of multivariate statistical analysis using the method of principal
components of the developed factor model made it possible to solve the problem of
identifying and interpreting latent factors affecting the basic indicators, to identify the
most significant factors, to ensure their grouping and structuring, as well as to reduce
the dimension of the problem being solved, which made it possible to analyze its
results.
The results obtained on the basis of the developed models made it possible to
formulate a list of activities and substantiate the feasibility of their implementation in
order to solve the problem of achieving the specified indicators of the university's
activity.
The proposed approach is new. The obtained estimates of the correlation
dependences between latent factors and basic indicators, the results of identifying
latent factors formed the basis for constructing a cognitive model of scenario
forecasting of measures to achieve the required values of the target indicators of the
university's activity in the international institutional ranking QS.
5. Acknowledgments
The article was prepared with the support of the Russian Foundation for Basic
Research, grants No. 18-07-00918, 19-07-01137 and 20-07-00926.
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