1. bookVolume 9 (2019): Edizione 1 (December 2019)
Dettagli della rivista
License
Formato
Rivista
eISSN
2084-1264
Prima pubblicazione
09 Apr 2014
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
Accesso libero

An Analysis of Field Preferences of an Educational System

Pubblicato online: 11 Jun 2020
Volume & Edizione: Volume 9 (2019) - Edizione 1 (December 2019)
Pagine: 26 - 45
Dettagli della rivista
License
Formato
Rivista
eISSN
2084-1264
Prima pubblicazione
09 Apr 2014
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
Introduction

In pursuit of liberating from different kinds of dependencies and prejudice, it was characteristic for different social groups, including women, to aim at getting full social and political rights. The most important thing from the activities of emancipational movement amongst women was to get the rights to educate at university level. Some more liberal factions in Europe were prone to give women the right to educate; however, there was strong opposition in case of allowing women to perform certain occupations connected with education.

J Hulewicz, H Więckowska, Z dziejów dopuszczenia kobiet do wyższych uczelni (1937).

Nevertheless, with time, in Europe, in the nineteenth century, this movement gained more followers and votes to change a social and political situation of women, which was a beginning of a new model of bringing up women.

J Suchmiel, Emancypacja naukowa kobiet w uniwersytetach w Krakowie i we Lwowie do roku 1939 (2004).

In nineteenth century, a model of bringing up women and what follows their access to education was dependent on the origins. Positivists have demanded to equate women's rights in many areas of public life, including women's access to higher education.

J Zawal, ‘Edukacja kobiet wczoraj i dziś’ (2006) 4 Edukacja Dorosłych 78–79.

Although, in the past 30 years of nineteenth century, some of the European universities allowed women to study, full involvement of women in teaching at universities started after the First World War. In Poland, the issue of a possibility to start studies and to perform an occupation connected with education was discussed at the turn of nineteenth and twentieth centuries. The main reason for a discussion in this area was a necessity of many women, connected with the results of regressive measures of invaders who not only repossessed fortune of Polish landed classes but also banished people from those lands and forced men to emigrate. As a consequence women faced the need to start a job that would give them money.

ibid.

There were few factors that were meaningful on Polish lands for an academic emancipational movement of women such as functioning of higher female courses, studies at foreign universities and a permission to study at Polish universities for women. A form of education that preceded regular higher education for women was courses of a university level. These courses, however, did not give any university diploma in spite of the level. By contrast, a university diploma could be gained at foreign universities that allowed for women's education. Many Polish women scientists at those times were educated at foreign universities. Their foreign education and professional successes were direct cause of an increase in educational needs and aspirations amongst Polish women. They also influenced movements and actions towards gaining women's rights to study at domestic universities, resulting mainly from the fact that foreign universities were impossible to afford.

ibid.

After the First World War, all faculties were accessible for women. Moreover, women could choose any levels of academic career together with a possibility to gain postdoctoral degree. Second decade of the past century showed a scope of performed transformations regarding a situation of women. Women's educational and professional activity became more common at that time. What's more, contemporary women fulfilled their needs at many new areas of human activity including political, social and economic area of life.

E Mazurek, ‘Kariera zawodowa i aktywność edukacyjna jako szansa samorozwoju’ [2007] Rocznik Andragoniczny 155.

Nowadays, it is nothing strange that academic rooms are full of women student and academic staff has women professors.

S Armstrong, Wojna kobiet, translated by B. Kucharuk (2015).

These days education becomes a criterion for an evaluation of personal and professional development for a contemporary woman, and it becomes a form of social activity. A contemporary woman wants to pursue her aims in any sphere of life without restricting only to being a mother and a wife; consequently, she wants to upgrade her academic qualifications, and it is very important not only because of economic reasons but mainly to express herself and to manifest her passions.

Zawal (n 3).

In addition, educated women more easily overcome structural barriers that, in any other stations, may prevent from gaining equal professional positions with men and have better motivation to broaden professional perspectives.

P Abbott, ‘Przebić szklany sufit: Promocja studiów kobiecych’ in Problematyka kobiet na świecie (1996).

At the same time, education provides possibilities to gain a new role and makes it easier for them to enter adult social life.

B Merrill, ‘Płeć, edukacja i uczenie się’ (tr M Machniewski) (2003) 1 Teraźniejszość -Człowiek - Edukacja.

The period of economic transformations and growth of social awareness created an image of a woman as an independent entity who possesses her own aspirations and aims. In addition, tendencies of women entering faculties of studies that have been dominated by men are visible. The aim of this article is to determine the number of women as students at 93 universities in Poland in the academic year 2016–2017, indicating their preferences according to the type of a university and an education group. For fulfilling the above-mentioned aim, an advanced tool of multi-dimensional comparative analysis (MCA) was used. This is the first research of this type in Poland.

Methodology of Research – A Tool of Multi-Dimensional Comparative Analysis

MCA is supposed to compare objects that are described with the usage of various characteristics. Very specific methods that are used for such analyses are the so-called taxonomic methods that are based on comparisons of objects with the usage of the so-called distance matrix.

W Pluta, Wielowymiarowa analiza porównawcza w badaniach ekonometrycznych (1977).

Amongst these methods, we can distinguish

grouping methods;

linear sorting.

In the first one, we can distinguish discrimination and classifying methods. By discrimination, we should understand an allocation of objects to familiar classes described by certain group of characteristics (such as position measures) or representatives (learning trial). On the other hand, classification is a division of objects into previously unknown classes in such a way that they are the most similar (in respect of distance) and objects from different classes were the least similar.

Ekonometria. Metody, przykłady, zadania, editor J. Dziechciarz (2003).

On the contrary, the aim of a linear sorting method is to sort objects from the best one to the worst one according to an accepted criterion of a compound phenomenon. During linear sorting, first, we need to determine objects, an aim of ranging and a set of characteristics that serve as a criterion for an evaluation. First stage of ranging is to choose statistical characteristics. In each analysis of this type, a proper choice of diagnostic characteristics that define described phenomenon is vital and has an influence on it. The choice of these characteristics should be based on the presumptions that both content-related and formal and properly chosen diagnostic variables should

P Gibas, K. Heffner, Analiza ekonomiczno przestrzenna (2007).

play a major role in a description of an analysed phenomenon;

be complete and accessible;

be captured in scales: interval or quotient;

be poorly correlated with each other to avoid information duplication;

be characterised by high level of changeability.

After considering content-related criteria, variables may undergo further reduction because in this set there should not be, simultaneously, characteristics that duplicate the information.

Consequently, similarities of characteristics are defined based on the matrix of Pearson's correlation coefficient.

According to the subject literature, diagnostic characteristics that a ranking will be based on should be characterized by

Zastosowanie metod ekonometryczno- statystycznych w zarządzaniu finansami zakładów ubezpieczeń, editor W. Ronka Chmielowiec (2004).

a weak correlation with each other,

a strong correlation with other characteristics that were not chosen to a final set of diagnostic variables.

A terminal value that serves to separate characteristics that are weakly or strongly correlated with each other and used in a procedure of variable choice is a critical value of correlation coefficient that defines vitality of correlation:

ibid.

r*=tα2tα2+n2,{r^*} = \sqrt {{{t_{\rm{\alpha}}^2} \over {t_{\rm{\alpha}}^2 + n - 2}}}, where t is the value of statistics that is taken from test charts of t- student for a given significance level α and for (n − 2) of level degrees of freedom; n is the number of defining variables.

On the basis of such reduction, we receive the so-called optimal set of diagnostic characteristics.

Another step of ranging is defining a character of particular variables. Amongst these, we can distinguish

Dziechciarz (n 12).

stimulant: an increase of which causes an increase in analysed phenomenon;

destimulants: an increase of which causes a decrease in the level of compound phenomenon;

nominants, their defined value (N) indicates that there is the highest level of compound phenomenon;

neutral, an increase or decrease of which has no influence on the level of compound phenomenon.

One of the elementary steps of taxonomic research is to make sure that there are only characteristics of a simulative kind in a set of diagnostic variables. Owing to this, a change of characters of all variables into simulative is required. This procedure is broadly described in a text by Dziechciarz.

ibid.

Another very essential step in conducted ranging is normalisation of variables. The aim of this is to deprive all variables of their label and to standardise their size. A process of normalisation of variables uses standardisation formulas and unification for variables measured in an interval scale and quotient transformations for variables measured on a quotient scale. The most often used technique of normalisation is standardisation, which is defined as

Zastosowanie metod ekonometryczno- statystycznych w zarządzaniu finansami zakładów ubezpieczeń, editor W. Ronka Chmielowiec (2004).

Zij=XijX¯jSj{Z_{ij}} = {{{X_{ij}} - {{\bar X}_j}} \over {{S_j}}} where Zij is the standardised value of j variable for i object, X¯j{\bar X_j} is the arithmetic mean of j variable, Sj is the standard deviation of j variable.

In a method of a model development, variables are standardised and are of stimulant character.

After such standardisation, variables become uniform because of the variability with standard deviation 1 and mean 0.

The next step of a research is to determine a pattern and anti-pattern for abstract objects.

K Nermend, Metody analizy wilokryterialnej i wielowymiarowej we wspomaganiu decyzji, (2017) 151–152.

A pattern is a vector of the highest values of coordinates and an anti-pattern is a vector whose coordinates are the lowest values of each variable. In the next step, a similarity of objects with the best abstract object is analysed through measuring a distance (e.g. Euclidean) for each pattern of development:

Dziechciarz (n 12) 70–80.

di0=j=1k(ZijZ0j)2i=1,2,,n{d_{i0}} = \sqrt {\sum\nolimits_{j = 1}^k {{{\left({{Z_{ij}} - {Z_{0j}}} \right)}^2}}} {\kern 15pt}i = 1,2, \ldots,n where di0 is the Euclidean distance of i object from a development pattern Z0.

The smaller the distance of the object from a pattern, the higher is the level of a complex phenomenon.

The last step of ranging is to determine the so-called development measure for each object: mi=1di0d0,(i=1,2,n),{m_i} = 1 - {{{d_{i0}}} \over {d0}},(i = 1,2, \ldots n), where mi is the measure of development for i object, d0 is the distance between a development pattern and an anti-pattern.

This measure is composed in such a way that its values are from [0,1] interval, and the higher is the value, the higher the level of a complex phenomenon.

Owing to the fact that taxonomic measures of a development replace a description of an analysed object with the help of many characteristics due to one aggregated value, a classification of socio-economic objects may be reduced to a division of objects based on the only one variable. A starting point for this simple method of classification is a set of objects segregated according to non-decreasing measure of a development value. On the basis of location parameters and dispersion data, an average value and a standard deviation of development measure, we can divide a set of objects into four subsets that include objects that belong to the following range [Nowak 1990, p. 92–93]

E Nowak, Metody taksonomiczne w klasyfikacji obiektów społeczno-gospodarczych (1990) 25–27.

:

group I: ziz¯+sz{z_i} \ge \bar z + {s_z} ,

group II: z¯+sz>ziz¯\bar z + {s_z} > {z_i} \ge \bar z ,

group III: z¯>ziz¯sz\bar z > {z_i} \ge \bar z - {s_z} ,

group IV: zi<z¯sz{z_i} < \bar z - {s_z} .

Description of Data And Results of Conducted Researches

A situation of working women at public universities was analysed based on 93 universities in Poland according to their profile: universities, universities of technology, universities of economics, universities of environmental and life sciences, university schools of physical education, medical universities, university schools of music, academies of art and design and military universities (Table 1).

A list of public universities in academic year 2016–2017 used in the analysis

SymbolUniversities
U1UNIVERSITY OF WROCŁAW
U2KAZIMIERZ WIELKI UNIVERSITY IN BYDGOSZCZ
U3NICOLAUS COPERNICUS UNIVERSITY OF TORUŃ
U4MARIA CURIE-SKŁODOWSKA UNIVERSITY IN LUBLIN
U5THE JOHN PAUL II CATHOLIC UNIVERSITY OF LUBLIN
U6UNIVERSITY OF ZIELONA GÓRA
U7UNIVERSITY OF ŁÓDŹ
U8JAGIELLONIAN UNIVERSITY IN KRAKÓW
U9UNIVERSITY OF WARSAW
U10CARDINAL WYSZYŃSKI UNIVERSITY IN WARSAW
U11UNIVERSITY OF OPOLE
U12UNIVERSITY OF RZESZÓW
U13UNIVERSITY OF BIAŁYSTOK
U14UNIVERSITY OF GDAŃSK
U15UNIWERSYTET ŚLĄSKI W KATOWICACH
U16THE JAN DŁUGOSZ UNIVERSITY IN CZĘSTOCHOWA
U17THE JAN KOCHANOWSKI UNIVERSITY IN KIELCE
U18UNIVERSITY OF WARMIA I MAZURY IN OLSZTYN
U19ADAM MICKIEWICZ UNIVERSITY IN POZNAŃ
U20UNIVERSITY OF SZCZECIN
U21WEST POMERANIAN UNIVERSITY OF TECHNOLOGY IN SZCZECIN
T1WROCŁAW UNIVERSITY OF TECHNOLOGY
T2LUBLIN UNIVERSITY OF TECHNOLOGY
T3LODZ UNIVERSITY OF TECHNOLOGY
T4AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY
T5TADEUSZ KOŚCIUSZKO CRACOW UNIVERSITY OF TECHNOLOGY
T6WARSAW UNIVERSITY OF TECHNOLOGY
T7OPOLE UNIVERSITY OF TECHNOLOGY
T8RZESZÓW UNIVERSITY OF TECHNOLOGY
T9BIAŁYSTOK UNIVERSITY OF TECHNOLOGY
T10GDAŃSK UNIVERSITY OF TECHNOLOGY
T11CZĘSTOCHOWA UNIVERSITY OF TECHNOLOGY
T12SILESIAN UNIVERSITY OF TECHNOLOGY
T13UNIVERSITY OF BIELSKO-BIALA
T14KIELCE UNIVERSITY OF TECHNOLOGY
T15POZNAŃ UNIVERSITY OF TECHNOLOGY
T16KOSZALIN UNIVERSITY OF TECHNOLOGY
T17MARITIME UNIVERSITY OF SZCZECIN
P1WROCŁAW UNIVERSITY OF ENVIRONMENTAL AND LIFE SCIENCES
P2UTP UNIVERSITY OF SCIENCE AND TECHNOLOGY IN BYDOSZCZ
P3UNIVERSITY OF LIFE SCIENCES IN LUBLIN
P4UNIVERSITY OF AGRICULTURE IN KRAKOW
P5WARSAW UNIVERSITY OF LIFE SCIENCES
P6POZNAŃ UNIVERSITY OF LIFE SCIENCES
P7SZCZECIN UNIVERSITY OF LIFE SCIENCES
E1WROCŁAW UNIVERSITY OF ECONOMICS
E2CRACOW UNIVERSITY ECONOMICS
E3WARSAW SCHOOL OF ECONOMICS
E4STATE HIGHER SCHOOL OF TECHNOLOGY AND ECONOMICS IN JAROSŁAW
E5UNIVERSITY OF ECONOMICS IN KATOWICE
E6POZNAŃ UNIVERSITY OF ECONOMICS
S1UNIVERSITY OF PHYSICAL EDUCATION IN WROCŁAW
S2UNIVERSITY OF PHYSICAL EDUCATION IN KRAKOW
S3JÓZEF PIŁSUDSKI UNIVERSITY OF PHYSICAL EDUCATION IN WARSAW
S4GDANSK UNIVERSITY OF PHYSICAL EDUCATION AND SPORT
S5THE JERZY KUKUCZKA UNIVERSITY OF PHYSICAL EDUCATION IN KATOWICE
S6THE EUGENIUSZ PIASECKI UNIVERSITY OF PHYSICAL EDUCATION IN POZNAN
M1WROCŁAW MEDICAL UNIVERSITY
M2MEDICAL UNIVERSITY OF LUBLIN
M3MEDICAL UNIVERSITY OF ŁÓDŹ
M4MEDICAL UNIVERSITY OF WARSAW
M5OPOLE MEDICAL SCHOOL
M6MEDICAL UNIVERSITY OF BIAŁYSTOK
M7MEDICAL UNIVERSITY OF GDAŃSK
M8POZNAŃ UNIVERSITY OF MEDICAL SCIENCES
M9POMERENIAN MEDICAL UNIVERSITY IN SZCZECIN
MU1THE KAROL LIPIŃSKI ACADEMY OF MUSIC IN WROCŁAW
MU2THE FELIKS NOWOWIEJSKI ACADEMY OF MUSIC IN BYDGOSZCZ
MU3ACADEMY OF MUSIC IN ŁÓDŹ
MU4ACADEMY OF MUSIC IN KRAKÓW
MU5THE FRYDERYK CHOPIN UNIVERSITY OF MUSIC G GDAŃSKG
MU6ACADEMY OF MUSIC IN GDAŃSK
MU7THE KAROL SZYMANOWSKI ACADEMY OF MUSIC IN KATOWICE
MU8ACADEMY OF MUSIC IN POZNAŃ
A1EUGENIUSZ GEPPERT ACADEMY OF ART AND DESIGN IN WROCLAW
A2THE STRZEMIŃSKI ACADEMY OF ART
A3ŁÓDŹ FILM SCHOOL
A4THE ACADEMY OF FINE ARTS IN KRAKOW
A5THE ACADEMY OF FINE ARTS IN WARSAW
A6THE ALEKSANDER ZELWEROWICZ NATIONAL ACADEMY OF DRAMATIC ART IN WARSAW
A7THE ACADEMY OF FINE ARTS IN GDAŃSK
A8THE KATOWICE ACADEMY OF FINE ARTS
A9UNIVERSITY OF THE ARTS IN POZNAN
PE1PEDAGOGICAL UNIVERSITY OF CRACOW
PE2THE ACADEMY OF PEDAGOGY IN WARSAW
PE3POMERANIAN UNIVERSITY IN SŁUPSK
W1POLISH NAVAL ACADEMY OF THE HEROS OF WESTERPLATTE IN GDYNIA
W2WAR STUDIES UNIVERSITY IN WARSAW
W3MILITARY ACADEMY OF TECHNOLOGY IN WARSAW
W4POLISH AIR FORCE ACADEMY
W5MILITARY UNIVERSITY OF LAND FORCES IN WROCŁAW
W6THE STATE FIRE SERVICE COLLEGE IN WARSAW
W7POLICE ACADEMY IN SZCZYTNO

Source: self-study.

Data used in this research refer to women on intramural and extra-mural studies and at bachelor's and master's studies in 2016–2017. The data were collected from the Central Statistical Office webpage.

https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/5488/8/4/1/szkolnictwo_wyzsze_dane_wstepne_stan_w_dniu_30_11_2016.xlsx.

In most of the cases, the percentage of women was higher than that of men both on intramural and extra-mural studies (Table 2 and Figure 1). The most feminist universities in 2016–2017 were medical universities because there were more than 85% of women students on intramural and extra-mural studies. A very similar result was observed at academies of art and design and schools of education.

Involvement of women in higher education in the academic year 2016–2017 according to the type of a university

Type of a universityIntramural studies (%)Extra-mural studies (%)
Universities65.2051.42
Universities of technology48.0029.72
Universities of environmental and life sciences62.2636.91
Universities of economics61.5550.63
University schools of physical education56.2742.08
Medical universities84.6284.51
University schools of music57.4863.18
Academy of art and design81.3471.67
School of education79.3273.61
Military universities39.1731.95

Source: self-study.

Figure 1

Involvement of women in higher-level education in the academic year 2016–2017 according to different types of universities

Source: self-study.

The universities that were rarely chosen by women were technical universities and military universities. At technical universities, there were about 48% and 40% at military universities on intramural studies. There were even fewer students on extra-mural studies of these types. As for technical universities, it amounted to about 30% of all students, and for military extra-mural studies, it was 31%.

It was also noticed that the biggest disproportion between intramural studies and extra-mural studies, regarding women, was at universities of environmental and life sciences. The difference between women studying on intramural and extra-mural studies amounted to 25 percentage points. Difference of more than 10 percentage points was noticed at technical universities, university schools of physical education and universities of economics. It is worth mentioning that only in case of university schools of music, the number of women studying on intramural studies was lower than in case of extra-mural studies.

In another step, using taxonomic measure of development, preferences of women according to a group of education were analysed. According to the CSO, each of university faculty can be assigned to one of the 10 categories, which is presented in Table 3.

Groups of teaching faculties

GroupName
1AGRICULTURE
2TECHNOLOGY, INDUSTRY, BUILDING
3BUSINESS, ADMINISTRATION AND LAW
4EDUCATION
5UMANISTIC SCIENCES AND ART
6NATURAL SCIENCES, MATHEMATICS AND STATISTICS
7SOCIAL SCIENCES, JOURNALISM AND INFORMATION
8TELEINFORMATION TECHNOLOGIES
9SERVICES
10HEALTH AND SOCIAL CARE

Source: CSO data.

For each university, a percentage of involvement of studying women at both levels and in each group of education was measured (diagnostic variables: X1, X2, X3, X4). Descriptive statistics of these variables are presented in Table 4.

Descriptive statistics of diagnostic variables

X1X2X3X4
Mean0.59720.62340.41510.4537
Standard deviation0.22230.24530.31720.3470
Variability coefficient37%39%76%76%

Source: self-study.

Owing to the fact that all variables influenced the situation of women in a simulative way at each university, the data were standardised in the first step and then the values of development measure for each university were distinguished according to the faculty groups (values of the pattern and anti-pattern form Table 5 were used).

Pattern and anti-pattern of diagnostic variables

X1X2X3X4
Pattern1.81201.53501.84381.5746
Anti-pattern−2.3895−2.5412−1.3085−1.3077

Source: self-study.

Dispersion of development measure together with basic descriptive measures is presented in Table 6 and Figure 2. The results are presented in Table 7.

Descriptive statistics of development measure

Descriptive statisticsValues
Mean0.5891
Standard deviation0.2325
Variability coefficient39.47%
Median0.6400
Q10.4637
Q30.7386
Skewness−0.6556
Kurtosis−0.2714
Max1.0000
Min0.0062

Source: self-study.

Figure 2

Bar chart and boxplot for a development measure

Source: self-study.

Ranking of universities in Poland in terms of women's involvement according to types of universities and education groups in the academic year 2016–2017

PlaceObjectDevelopment measurePlaceObjectDevelopment measurePlaceObjectDevelopment measurePlaceObjectDevelopment measure
1M6_91.0000099U3_50.73852197U3_70.63989295U15_90.46245
2M2_90.99232100U6_60.73757198P5_20.63911296U8_90.45451
3M3_90.98664101M8_60.73685199U14_30.63818297W3_70.45308
4U13_40.96683102U3_60.73678200U12_30.63769298P2_20.44546
5U14_40.95816103U4_20.73671201P3_10.63743299U4_60.44471
6E4_40.95303104U3_50.73643202E5_70.63686300S4_100.43861
7U13_100.94616105U5_100.73523203U2_60.63509301W1_90.43716
8M8_20.94534106U9_30.73452204P1_10.63480302T3_20.43638
9U3_40.94398107U2_30.73243205Pe3_50.63435303S1_40.42516
10Pe1_100.94389108U13_50.73242206T3_60.63411304W3_30.42456
11T13_40.94259109U7_50.73039207U13_70.63138305T13_20.42409
12U7_40.93738110U19_40.72793208E1_20.63092306T15_90.42346
13P5_40.93541111U9_60.72747209U13_60.63059307U20_20.42260
14Pe2_40.93322112U10_60.72710210P5_30.63019308T9_20.41998
15U3_40.93293113U6_90.72580211U18_60.62648309U5_20.40917
16U6_100.93180114U15_60.72362212U6_70.62520310T6_90.39818
17U1_40.93038115Pe3_60.72355213U6_30.62113311W2_70.39230
18U9_40.93004116U8_50.72266214T12_30.61805312T14_20.38815
19U10_40.92817117U14_50.72070215T11_30.61695313T10_20.37987
20U3_100.92400118S1_100.71908216U12_90.61674314E6_80.37413
21U12_40.92332119T8_60.71882217E6_30.61347315U11_10.36205
22U4_40.91150120U3_30.71881218P2_30.61278316T16_20.36009
23U8_100.91115121T11_60.71828219E4_30.61258317S5_40.35937
24U18_40.91012122U18_50.71822220U8_30.61199318T17_90.35755
25Pe1_40.90933123P6_70.71579221MU8_40.61157319T17_30.35203
26T3_50.90718124Pe1_90.71558222T10_70.60865320T8_20.35186
27P5_100.90521125S2_90.71426223U10_20.60816321S4_40.34922
28U7_100.90029126U11_50.71181224U5_60.60595322T12_20.34904
29P6_100.89214127U7_30.71069225U18_20.60363323S2_40.34826
30U1_100.88997128U12_50.70953226U10_90.60358324T15_20.34480
31U12_100.88312129T4_50.70905227T10_30.60309325U3_20.34365
32U17_40.88310130U11_70.70891228MU3_50.60219326T8_90.33906
33U14_100.88251131U16_50.70820229T4_60.59756327T4_20.33489
34U8_40.88179132E2_30.70704230S5_90.59541328T1_20.33346
35P4_100.88049133U17_60.70617231U3_70.59414329T6_20.33035
36Pe1_30.87746134U5_40.70616232T2_30.59404330T2_20.32399
37T15_50.87338135M3_20.70385233U5_30.59389331P5_80.32222
38M5_100.87207136U20_10.70376234U11_20.59360332W7_90.31659
39M1_100.87194137P7_10.70376235T16_70.59080333T12_90.30743
40P3_100.87184138U1_30.70290236U3_90.58769334M8_90.30361
41T16_40.86977139U19_60.70027237U18_90.58709335U2_100.30312
42Pe3_40.86873140U19_50.70022238U5_70.58548336U19_80.29661
43M4_100.86820141U12_70.69978239E2_70.58541337P1_30.29430
44T12_50.86616142U4_50.69920240U3_90.58473338E1_80.29254
45A1_20.86317143S2_100.69815241E1_70.58225339T11_20.28840
46A6_50.85570144P5_60.69746242E6_70.57937340P2_90.28014
47M8_100.84907145U2_50.69678243U17_70.57617341T2_90.26776
48P2_50.84651146P5_90.69671244U8_20.57102342U13_80.25397
49M7_100.84027147T6_30.69578245T4_30.56884343E5_80.23941
50U3_100.83471148T6_70.69567246S4_90.56684344T7_40.23351
51U4_100.83469149U3_60.69539247T3_30.56606345P4_30.23227
52U18_100.83286150U17_30.69509248T15_30.56560346P3_70.23059
53U6_40.83184151U5_50.69186249W3_60.55541347T3_90.22784
54A2_50.82963152U10_30.69038250P4_10.55413348U2_80.22704
55A7_50.82743153U20_70.68990251P5_70.55352349W3_20.22643
56M6_100.82739154P7_70.68990252S6_40.55063350U8_80.22290
57A4_50.82459155T1_60.68819253MU6_50.54920351U9_90.21790
58A1_50.82367156U11_40.68711254U19_90.54779352T3_70.21760
59U16_100.82041157E5_30.68710255MU3_40.54594353T4_40.21724
60M2_100.82006158U16_30.68585256U16_90.54412354U5_90.21701
61U16_40.81788159P1_20.68482257MU5_50.54237355W6_90.21248
62E1_90.81623160P2_10.68219258U8_60.54168356T7_20.21150
63T13_100.81489161P4_70.67823259MU1_50.53989357P1_90.20909
64M9_100.81076162U8_70.67380260MU5_40.53869358U6_80.20281
65Pe1_50.80448163T8_30.67261261U17_90.53553359T4_80.19577
66A8_50.80186164P3_20.67128262T9_30.53530360U2_20.18769
67MU6_40.80061165U3_30.67049263MU4_40.53239361W5_60.17474
68M3_100.79961166U13_30.67002264S5_30.52803362U3_20.16983
69U6_50.79787167P1_70.66895265P5_10.52449363U18_80.16872
70A9_50.79263168U14_70.66874266T1_30.52431364U3_80.16302
71P2_60.78265169U7_70.66818267S1_90.52409365U9_80.16299
72Pe1_60.78072170U4_70.66599268W4_90.51830366T10_80.12593
73T5_60.77978171S6_100.66416269U15_70.51772367T3_80.11880
74P4_60.77975172U9_50.66293270T11_90.51680368W3_80.11487
75A5_50.77692173U11_90.66171271MU8_50.51654369U10_80.11429
76T16_50.77149174E2_20.66069272W5_90.51646370T1_80.11177
77T13_50.77037175T7_100.65947273U4_90.51642371U12_80.11086
78P1_60.76706176P4_20.65929274S6_90.51604372T2_80.10586
79P6_60.76458177S5_100.65910275U7_20.51440373T9_80.10583
80T9_50.76441178A3_50.65831276MU7_50.51180374T6_80.09700
81T7_70.76351179U16_70.65816277MU2_40.51052375U20_80.09444
82Pe2_70.75918180U18_30.65653278W1_70.50407376P7_80.09444
83U15_30.75890181U19_30.65651279U17_10.50282377U1_80.09377
84T14_70.75743182U1_60.65579280MU2_50.50244378U3_80.09089
85U2_40.75590183U7_60.65562281P6_10.50048379T12_80.09014
86U1_50.75420184U17_50.65462282MU4_50.49992380T11_80.08855
87U16_60.75412185T13_30.65419283S3_90.49852381T14_80.08754
88U15_40.75364186U2_70.65338284E3_30.49815382T5_80.08248
89U12_60.75336187T16_30.65256285T6_60.48760383T15_80.07126
90E2_90.75264188U4_30.64845286U17_20.47721384U4_80.06961
91P7_60.74777189U14_60.64654287T5_20.47559385U11_80.06133
92P3_60.74621190E1_30.64543288S3_40.46922386T17_20.05473
93Pe1_70.74431191U1_70.64490289Pe3_70.46782387T7_80.04429
94U11_30.74406192S3_100.64377290W2_90.46640388T8_80.03898
95U11_100.74379193P6_20.64276291E3_70.46573389T16_80.02980
96T9_60.74198194U10_70.64131292P3_90.46468390W1_20.02734
97U11_60.74068195U18_70.64054293P7_20.46408391P2_80.00622
98U10_50.73875196U9_70.64003294W2_20.46335

Source: self-study

What is visible is a left-side asymmetry of development measure dispersion which means that such groups exist for which an involvement of women students in studying groups is lower than average expressed with a median of a development measure value. Another thing that states about an asymmetry is a discrepancy in the value of a median and average value equal to 0.59. Values of analysed measure can be characterised by a high variability (at a level of 39%), which means that there is a high differentiation between education groups chosen by women.

On the basis of the obtained data, it is visible that the most feministic education groups are those concerning health and social care. In these groups, high values of development measure were visible, which is explained by an average value of a measure and also by median. In a group with faculties connected with health and social care, there are people who want to become a therapist, a rehabilitator and a social worker as well as a speech therapist and a nurse. It is not surprising because these are usually women who work on this kind of positions. Similarly, high values of development measure in a group of faculties referring to staff education raise no doubt. In case of this group, regardless of the type of university, women are also dominant.

Despite of little dispersion of data (15%), a group with humanistic and art faculties were dominated by women. The lower value of development measure for this group was almost equal to 0.5, which is the best result amongst the rest of subsets faculty groups. In this group, the most common are universities, academies of art and design and university schools of music.

Figure 3 and Table 8 show a layout of development measure in particular education groups.

Figure 3

Boxplot for a development measure in particular education group

Source: self-study.

Descriptive statistics of a development measure in particular education groups

MeasuresGroup 1Group 2Group 3Group 4Group 5Group 6Group 7Group 8Group 9Group 10
Mean0.58060.45850.62050.73710.71800.67670.60850.13980.51740.8034
Standard deviation0.11100.19780.11570.22820.10610.11450.12010.08740.20220.1352
Variability coefficient19.12%43.15%18.65%30.96%14.77%16.92%19.74%62.54%39.08%16.84%
Median0.59450.43020.63820.83180.72650.71860.64000.11090.51660.8347
Q10.50820.34150.59400.54590.67020.63480.58080.08850.36770.7438
Q30.67100.61380.68870.93000.79660.74300.67600.19580.60150.8831
Skewness−0.65880.1420−1.3933−0.8745−0.5759−2.5503−1.66860.99320.5436−2.0373
Kurtosis−0.09210.05993.2724−0.5254−0.27008.78033.37080.37150.23575.0072
Max0.70380.94530.87750.96680.90720.78260.76350.37411.00000.9462
Min0.36210.02730.23230.21720.49990.17470.21760.00620.20910.3031

Source: self-study.

It is worth noticing that there was a huge dispersion of data in education groups with faculties of technology, industry and building. Nevertheless, we need to highlight that women chose studies on faculties in this group between general universities and universities of environmental and life sciences (a development measure for most of these universities was 0.45) than amongst universities of technology and military universities (for these kind of universities, a calculated factor gained low values, and these universities were at the end places of the ranking). It is usually said that universities of technology or science faculties are not a domain of women, which was confirmed in this case.

It is also noticeable that the least attractive amongst women students are faculties connected with teleinformation technology. These are information technology, information science, creating and analyses of programming and application or education of information and technology. An average value of a development measure in this group was much lower than that in the remaining groups, and a maximal value of calculated development measure in this group was over 0.4.

It is interesting that a group of services was characterised by a huge dispersion of results. Medical universities that offer education in the sphere of services have majority of women students (at the Medical University in Bialystok, only women students were studying, not far from that was the Medical University in Lublin with a result of 0.99%). This disproportion in not surprising because medical universities offer, in their scope of services, cosmetology and hair care, which are very popular nowadays. At the remaining universities, in most of the cases, these are tourism, security and property protection. Owing to the fact that a scope of faculties with services is huge, the result is not surprising.

A final result of the aforementioned analysis is a ranking list in terms of women's involvement according to education group at universities listed in Table 5.

Conclusion

The equality of chances in a sector of higher education is one of the existing elements of the union policy. Statistic data from conducted analysis show that higher education became more accessible for women and these women dominated the people studying at this level of education.

As long as the number of women and men educating at higher level is rather equal (at some areas with dominance of women), the data on education profiles show significant differentiation amongst gender. Women still represent minority at profiles generally considered as ‘male’ (technology, industry, building, agriculture and science). They represent majority on ‘soft’ faculties (education, health and care, humanistic and art).

Figure 1

Involvement of women in higher-level education in the academic year 2016–2017 according to different types of universitiesSource: self-study.
Involvement of women in higher-level education in the academic year 2016–2017 according to different types of universitiesSource: self-study.

Figure 2

Bar chart and boxplot for a development measureSource: self-study.
Bar chart and boxplot for a development measureSource: self-study.

Figure 3

Boxplot for a development measure in particular education groupSource: self-study.
Boxplot for a development measure in particular education groupSource: self-study.

Ranking of universities in Poland in terms of women's involvement according to types of universities and education groups in the academic year 2016–2017

PlaceObjectDevelopment measurePlaceObjectDevelopment measurePlaceObjectDevelopment measurePlaceObjectDevelopment measure
1M6_91.0000099U3_50.73852197U3_70.63989295U15_90.46245
2M2_90.99232100U6_60.73757198P5_20.63911296U8_90.45451
3M3_90.98664101M8_60.73685199U14_30.63818297W3_70.45308
4U13_40.96683102U3_60.73678200U12_30.63769298P2_20.44546
5U14_40.95816103U4_20.73671201P3_10.63743299U4_60.44471
6E4_40.95303104U3_50.73643202E5_70.63686300S4_100.43861
7U13_100.94616105U5_100.73523203U2_60.63509301W1_90.43716
8M8_20.94534106U9_30.73452204P1_10.63480302T3_20.43638
9U3_40.94398107U2_30.73243205Pe3_50.63435303S1_40.42516
10Pe1_100.94389108U13_50.73242206T3_60.63411304W3_30.42456
11T13_40.94259109U7_50.73039207U13_70.63138305T13_20.42409
12U7_40.93738110U19_40.72793208E1_20.63092306T15_90.42346
13P5_40.93541111U9_60.72747209U13_60.63059307U20_20.42260
14Pe2_40.93322112U10_60.72710210P5_30.63019308T9_20.41998
15U3_40.93293113U6_90.72580211U18_60.62648309U5_20.40917
16U6_100.93180114U15_60.72362212U6_70.62520310T6_90.39818
17U1_40.93038115Pe3_60.72355213U6_30.62113311W2_70.39230
18U9_40.93004116U8_50.72266214T12_30.61805312T14_20.38815
19U10_40.92817117U14_50.72070215T11_30.61695313T10_20.37987
20U3_100.92400118S1_100.71908216U12_90.61674314E6_80.37413
21U12_40.92332119T8_60.71882217E6_30.61347315U11_10.36205
22U4_40.91150120U3_30.71881218P2_30.61278316T16_20.36009
23U8_100.91115121T11_60.71828219E4_30.61258317S5_40.35937
24U18_40.91012122U18_50.71822220U8_30.61199318T17_90.35755
25Pe1_40.90933123P6_70.71579221MU8_40.61157319T17_30.35203
26T3_50.90718124Pe1_90.71558222T10_70.60865320T8_20.35186
27P5_100.90521125S2_90.71426223U10_20.60816321S4_40.34922
28U7_100.90029126U11_50.71181224U5_60.60595322T12_20.34904
29P6_100.89214127U7_30.71069225U18_20.60363323S2_40.34826
30U1_100.88997128U12_50.70953226U10_90.60358324T15_20.34480
31U12_100.88312129T4_50.70905227T10_30.60309325U3_20.34365
32U17_40.88310130U11_70.70891228MU3_50.60219326T8_90.33906
33U14_100.88251131U16_50.70820229T4_60.59756327T4_20.33489
34U8_40.88179132E2_30.70704230S5_90.59541328T1_20.33346
35P4_100.88049133U17_60.70617231U3_70.59414329T6_20.33035
36Pe1_30.87746134U5_40.70616232T2_30.59404330T2_20.32399
37T15_50.87338135M3_20.70385233U5_30.59389331P5_80.32222
38M5_100.87207136U20_10.70376234U11_20.59360332W7_90.31659
39M1_100.87194137P7_10.70376235T16_70.59080333T12_90.30743
40P3_100.87184138U1_30.70290236U3_90.58769334M8_90.30361
41T16_40.86977139U19_60.70027237U18_90.58709335U2_100.30312
42Pe3_40.86873140U19_50.70022238U5_70.58548336U19_80.29661
43M4_100.86820141U12_70.69978239E2_70.58541337P1_30.29430
44T12_50.86616142U4_50.69920240U3_90.58473338E1_80.29254
45A1_20.86317143S2_100.69815241E1_70.58225339T11_20.28840
46A6_50.85570144P5_60.69746242E6_70.57937340P2_90.28014
47M8_100.84907145U2_50.69678243U17_70.57617341T2_90.26776
48P2_50.84651146P5_90.69671244U8_20.57102342U13_80.25397
49M7_100.84027147T6_30.69578245T4_30.56884343E5_80.23941
50U3_100.83471148T6_70.69567246S4_90.56684344T7_40.23351
51U4_100.83469149U3_60.69539247T3_30.56606345P4_30.23227
52U18_100.83286150U17_30.69509248T15_30.56560346P3_70.23059
53U6_40.83184151U5_50.69186249W3_60.55541347T3_90.22784
54A2_50.82963152U10_30.69038250P4_10.55413348U2_80.22704
55A7_50.82743153U20_70.68990251P5_70.55352349W3_20.22643
56M6_100.82739154P7_70.68990252S6_40.55063350U8_80.22290
57A4_50.82459155T1_60.68819253MU6_50.54920351U9_90.21790
58A1_50.82367156U11_40.68711254U19_90.54779352T3_70.21760
59U16_100.82041157E5_30.68710255MU3_40.54594353T4_40.21724
60M2_100.82006158U16_30.68585256U16_90.54412354U5_90.21701
61U16_40.81788159P1_20.68482257MU5_50.54237355W6_90.21248
62E1_90.81623160P2_10.68219258U8_60.54168356T7_20.21150
63T13_100.81489161P4_70.67823259MU1_50.53989357P1_90.20909
64M9_100.81076162U8_70.67380260MU5_40.53869358U6_80.20281
65Pe1_50.80448163T8_30.67261261U17_90.53553359T4_80.19577
66A8_50.80186164P3_20.67128262T9_30.53530360U2_20.18769
67MU6_40.80061165U3_30.67049263MU4_40.53239361W5_60.17474
68M3_100.79961166U13_30.67002264S5_30.52803362U3_20.16983
69U6_50.79787167P1_70.66895265P5_10.52449363U18_80.16872
70A9_50.79263168U14_70.66874266T1_30.52431364U3_80.16302
71P2_60.78265169U7_70.66818267S1_90.52409365U9_80.16299
72Pe1_60.78072170U4_70.66599268W4_90.51830366T10_80.12593
73T5_60.77978171S6_100.66416269U15_70.51772367T3_80.11880
74P4_60.77975172U9_50.66293270T11_90.51680368W3_80.11487
75A5_50.77692173U11_90.66171271MU8_50.51654369U10_80.11429
76T16_50.77149174E2_20.66069272W5_90.51646370T1_80.11177
77T13_50.77037175T7_100.65947273U4_90.51642371U12_80.11086
78P1_60.76706176P4_20.65929274S6_90.51604372T2_80.10586
79P6_60.76458177S5_100.65910275U7_20.51440373T9_80.10583
80T9_50.76441178A3_50.65831276MU7_50.51180374T6_80.09700
81T7_70.76351179U16_70.65816277MU2_40.51052375U20_80.09444
82Pe2_70.75918180U18_30.65653278W1_70.50407376P7_80.09444
83U15_30.75890181U19_30.65651279U17_10.50282377U1_80.09377
84T14_70.75743182U1_60.65579280MU2_50.50244378U3_80.09089
85U2_40.75590183U7_60.65562281P6_10.50048379T12_80.09014
86U1_50.75420184U17_50.65462282MU4_50.49992380T11_80.08855
87U16_60.75412185T13_30.65419283S3_90.49852381T14_80.08754
88U15_40.75364186U2_70.65338284E3_30.49815382T5_80.08248
89U12_60.75336187T16_30.65256285T6_60.48760383T15_80.07126
90E2_90.75264188U4_30.64845286U17_20.47721384U4_80.06961
91P7_60.74777189U14_60.64654287T5_20.47559385U11_80.06133
92P3_60.74621190E1_30.64543288S3_40.46922386T17_20.05473
93Pe1_70.74431191U1_70.64490289Pe3_70.46782387T7_80.04429
94U11_30.74406192S3_100.64377290W2_90.46640388T8_80.03898
95U11_100.74379193P6_20.64276291E3_70.46573389T16_80.02980
96T9_60.74198194U10_70.64131292P3_90.46468390W1_20.02734
97U11_60.74068195U18_70.64054293P7_20.46408391P2_80.00622
98U10_50.73875196U9_70.64003294W2_20.46335

Descriptive statistics of a development measure in particular education groups

MeasuresGroup 1Group 2Group 3Group 4Group 5Group 6Group 7Group 8Group 9Group 10
Mean0.58060.45850.62050.73710.71800.67670.60850.13980.51740.8034
Standard deviation0.11100.19780.11570.22820.10610.11450.12010.08740.20220.1352
Variability coefficient19.12%43.15%18.65%30.96%14.77%16.92%19.74%62.54%39.08%16.84%
Median0.59450.43020.63820.83180.72650.71860.64000.11090.51660.8347
Q10.50820.34150.59400.54590.67020.63480.58080.08850.36770.7438
Q30.67100.61380.68870.93000.79660.74300.67600.19580.60150.8831
Skewness−0.65880.1420−1.3933−0.8745−0.5759−2.5503−1.66860.99320.5436−2.0373
Kurtosis−0.09210.05993.2724−0.5254−0.27008.78033.37080.37150.23575.0072
Max0.70380.94530.87750.96680.90720.78260.76350.37411.00000.9462
Min0.36210.02730.23230.21720.49990.17470.21760.00620.20910.3031

Descriptive statistics of diagnostic variables

X1X2X3X4
Mean0.59720.62340.41510.4537
Standard deviation0.22230.24530.31720.3470
Variability coefficient37%39%76%76%

Involvement of women in higher education in the academic year 2016–2017 according to the type of a university

Type of a universityIntramural studies (%)Extra-mural studies (%)
Universities65.2051.42
Universities of technology48.0029.72
Universities of environmental and life sciences62.2636.91
Universities of economics61.5550.63
University schools of physical education56.2742.08
Medical universities84.6284.51
University schools of music57.4863.18
Academy of art and design81.3471.67
School of education79.3273.61
Military universities39.1731.95

A list of public universities in academic year 2016–2017 used in the analysis

SymbolUniversities
U1UNIVERSITY OF WROCŁAW
U2KAZIMIERZ WIELKI UNIVERSITY IN BYDGOSZCZ
U3NICOLAUS COPERNICUS UNIVERSITY OF TORUŃ
U4MARIA CURIE-SKŁODOWSKA UNIVERSITY IN LUBLIN
U5THE JOHN PAUL II CATHOLIC UNIVERSITY OF LUBLIN
U6UNIVERSITY OF ZIELONA GÓRA
U7UNIVERSITY OF ŁÓDŹ
U8JAGIELLONIAN UNIVERSITY IN KRAKÓW
U9UNIVERSITY OF WARSAW
U10CARDINAL WYSZYŃSKI UNIVERSITY IN WARSAW
U11UNIVERSITY OF OPOLE
U12UNIVERSITY OF RZESZÓW
U13UNIVERSITY OF BIAŁYSTOK
U14UNIVERSITY OF GDAŃSK
U15UNIWERSYTET ŚLĄSKI W KATOWICACH
U16THE JAN DŁUGOSZ UNIVERSITY IN CZĘSTOCHOWA
U17THE JAN KOCHANOWSKI UNIVERSITY IN KIELCE
U18UNIVERSITY OF WARMIA I MAZURY IN OLSZTYN
U19ADAM MICKIEWICZ UNIVERSITY IN POZNAŃ
U20UNIVERSITY OF SZCZECIN
U21WEST POMERANIAN UNIVERSITY OF TECHNOLOGY IN SZCZECIN
T1WROCŁAW UNIVERSITY OF TECHNOLOGY
T2LUBLIN UNIVERSITY OF TECHNOLOGY
T3LODZ UNIVERSITY OF TECHNOLOGY
T4AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY
T5TADEUSZ KOŚCIUSZKO CRACOW UNIVERSITY OF TECHNOLOGY
T6WARSAW UNIVERSITY OF TECHNOLOGY
T7OPOLE UNIVERSITY OF TECHNOLOGY
T8RZESZÓW UNIVERSITY OF TECHNOLOGY
T9BIAŁYSTOK UNIVERSITY OF TECHNOLOGY
T10GDAŃSK UNIVERSITY OF TECHNOLOGY
T11CZĘSTOCHOWA UNIVERSITY OF TECHNOLOGY
T12SILESIAN UNIVERSITY OF TECHNOLOGY
T13UNIVERSITY OF BIELSKO-BIALA
T14KIELCE UNIVERSITY OF TECHNOLOGY
T15POZNAŃ UNIVERSITY OF TECHNOLOGY
T16KOSZALIN UNIVERSITY OF TECHNOLOGY
T17MARITIME UNIVERSITY OF SZCZECIN
P1WROCŁAW UNIVERSITY OF ENVIRONMENTAL AND LIFE SCIENCES
P2UTP UNIVERSITY OF SCIENCE AND TECHNOLOGY IN BYDOSZCZ
P3UNIVERSITY OF LIFE SCIENCES IN LUBLIN
P4UNIVERSITY OF AGRICULTURE IN KRAKOW
P5WARSAW UNIVERSITY OF LIFE SCIENCES
P6POZNAŃ UNIVERSITY OF LIFE SCIENCES
P7SZCZECIN UNIVERSITY OF LIFE SCIENCES
E1WROCŁAW UNIVERSITY OF ECONOMICS
E2CRACOW UNIVERSITY ECONOMICS
E3WARSAW SCHOOL OF ECONOMICS
E4STATE HIGHER SCHOOL OF TECHNOLOGY AND ECONOMICS IN JAROSŁAW
E5UNIVERSITY OF ECONOMICS IN KATOWICE
E6POZNAŃ UNIVERSITY OF ECONOMICS
S1UNIVERSITY OF PHYSICAL EDUCATION IN WROCŁAW
S2UNIVERSITY OF PHYSICAL EDUCATION IN KRAKOW
S3JÓZEF PIŁSUDSKI UNIVERSITY OF PHYSICAL EDUCATION IN WARSAW
S4GDANSK UNIVERSITY OF PHYSICAL EDUCATION AND SPORT
S5THE JERZY KUKUCZKA UNIVERSITY OF PHYSICAL EDUCATION IN KATOWICE
S6THE EUGENIUSZ PIASECKI UNIVERSITY OF PHYSICAL EDUCATION IN POZNAN
M1WROCŁAW MEDICAL UNIVERSITY
M2MEDICAL UNIVERSITY OF LUBLIN
M3MEDICAL UNIVERSITY OF ŁÓDŹ
M4MEDICAL UNIVERSITY OF WARSAW
M5OPOLE MEDICAL SCHOOL
M6MEDICAL UNIVERSITY OF BIAŁYSTOK
M7MEDICAL UNIVERSITY OF GDAŃSK
M8POZNAŃ UNIVERSITY OF MEDICAL SCIENCES
M9POMERENIAN MEDICAL UNIVERSITY IN SZCZECIN
MU1THE KAROL LIPIŃSKI ACADEMY OF MUSIC IN WROCŁAW
MU2THE FELIKS NOWOWIEJSKI ACADEMY OF MUSIC IN BYDGOSZCZ
MU3ACADEMY OF MUSIC IN ŁÓDŹ
MU4ACADEMY OF MUSIC IN KRAKÓW
MU5THE FRYDERYK CHOPIN UNIVERSITY OF MUSIC G GDAŃSKG
MU6ACADEMY OF MUSIC IN GDAŃSK
MU7THE KAROL SZYMANOWSKI ACADEMY OF MUSIC IN KATOWICE
MU8ACADEMY OF MUSIC IN POZNAŃ
A1EUGENIUSZ GEPPERT ACADEMY OF ART AND DESIGN IN WROCLAW
A2THE STRZEMIŃSKI ACADEMY OF ART
A3ŁÓDŹ FILM SCHOOL
A4THE ACADEMY OF FINE ARTS IN KRAKOW
A5THE ACADEMY OF FINE ARTS IN WARSAW
A6THE ALEKSANDER ZELWEROWICZ NATIONAL ACADEMY OF DRAMATIC ART IN WARSAW
A7THE ACADEMY OF FINE ARTS IN GDAŃSK
A8THE KATOWICE ACADEMY OF FINE ARTS
A9UNIVERSITY OF THE ARTS IN POZNAN
PE1PEDAGOGICAL UNIVERSITY OF CRACOW
PE2THE ACADEMY OF PEDAGOGY IN WARSAW
PE3POMERANIAN UNIVERSITY IN SŁUPSK
W1POLISH NAVAL ACADEMY OF THE HEROS OF WESTERPLATTE IN GDYNIA
W2WAR STUDIES UNIVERSITY IN WARSAW
W3MILITARY ACADEMY OF TECHNOLOGY IN WARSAW
W4POLISH AIR FORCE ACADEMY
W5MILITARY UNIVERSITY OF LAND FORCES IN WROCŁAW
W6THE STATE FIRE SERVICE COLLEGE IN WARSAW
W7POLICE ACADEMY IN SZCZYTNO

Descriptive statistics of development measure

Descriptive statisticsValues
Mean0.5891
Standard deviation0.2325
Variability coefficient39.47%
Median0.6400
Q10.4637
Q30.7386
Skewness−0.6556
Kurtosis−0.2714
Max1.0000
Min0.0062

Pattern and anti-pattern of diagnostic variables

X1X2X3X4
Pattern1.81201.53501.84381.5746
Anti-pattern−2.3895−2.5412−1.3085−1.3077

Groups of teaching faculties

GroupName
1AGRICULTURE
2TECHNOLOGY, INDUSTRY, BUILDING
3BUSINESS, ADMINISTRATION AND LAW
4EDUCATION
5UMANISTIC SCIENCES AND ART
6NATURAL SCIENCES, MATHEMATICS AND STATISTICS
7SOCIAL SCIENCES, JOURNALISM AND INFORMATION
8TELEINFORMATION TECHNOLOGIES
9SERVICES
10HEALTH AND SOCIAL CARE

P Abbott, ‘Przebić szklany sufit: Promocja studiów kobiecych’ in EH Oleksy (ed), Problematyka kobiet na świecie (1996)AbbottP‘Przebić szklany sufit: Promocja studiów kobiecych’inOleksyEH(ed),Problematyka kobiet na świecie1996Search in Google Scholar

S Armstrong, Wojna kobiet (B Kucharuk tr, Prószyński i S-ka, Warszawa 2015)ArmstrongSWojna kobietKucharukBtr,Prószyński i S-kaWarszawa2015Search in Google Scholar

J Dziechciarz, Ekonometria. Metody, przykłady, zadania (Wydawnictwo AE, Wrocław 2003)DziechciarzJEkonometria. Metody, przykłady, zadaniaWydawnictwo AEWrocław2003Search in Google Scholar

P Gibas and K Heffner, Analiza ekonomiczno przestrzenna (Wydawnictwo AE, Katowice 2007) https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/5488/8/4/1/szkolnictwo_wyzsze_dane_wstepne_stan_w_dniu_30_11_2016.xlsxGibasPHeffnerKAnaliza ekonomiczno przestrzennaWydawnictwo AEKatowice2007https://stat.gov.pl/download/gfx/portalinformacyjny/pl/defaultaktualnosci/5488/8/4/1/szkolnictwo_wyzsze_dane_wstepne_stan_w_dniu_30_11_2016.xlsxSearch in Google Scholar

J Hulewicz and H Więckowska, Z dziejów dopuszczenia kobiet do wyższych uczelni, Komunikat nr 11 Zarządu Głównego Stowarzyszenia Kobiet z Wyższym Wykształceniem, Warszawa (1937)HulewiczJWięckowskaHZ dziejów dopuszczenia kobiet do wyższych uczelniKomunikat nr 11 Zarządu Głównego Stowarzyszenia Kobiet z Wyższym Wykształceniem,Warszawa1937Search in Google Scholar

E Mazurek, ‘Kariera zawodowa i aktywność edukacyjna jako szansa samorozwoju’ (2007) Rocznik AndragogicznyMazurekE‘Kariera zawodowa i aktywność edukacyjna jako szansa samorozwoju’2007Rocznik AndragogicznySearch in Google Scholar

B Merrill, Płeć, edukacja i uczenie się (2003) 1 Teraźniejszość - Człowiek - Edukacja.MerrillBPłeć, edukacja i uczenie się20031Teraźniejszość - Człowiek - Edukacja.Search in Google Scholar

K Nermend, Metody analizy wilokryterialnej i wielowymiarowej we wspomaganiu decyzji (PWN, Warszawa 2017)NermendKMetody analizy wilokryterialnej i wielowymiarowej we wspomaganiu decyzjiPWNWarszawa2017Search in Google Scholar

E Nowak, Metody taksonomiczne w klasyfikacji obiektów społeczno-gospodarczych (PWE, Warszawa 1990)NowakEMetody taksonomiczne w klasyfikacji obiektów społeczno-gospodarczychPWEWarszawa1990Search in Google Scholar

W Pluta, Wielowymiarowa analiza porównawcza w badaniach ekonometrycznych (PWN, Warszawa 1977)PlutaWWielowymiarowa analiza porównawcza w badaniach ekonometrycznychPWNWarszawa1977Search in Google Scholar

J Suchmiel, ‘Emancypacja naukowa kobiet w uniwersytetach w Krakowie i we Lwowie do roku 1939’ (2004) Prace Naukowe Akademii im. Jana Długosza w CzęstochowieSuchmielJ‘Emancypacja naukowa kobiet w uniwersytetach w Krakowie i we Lwowie do roku 1939’2004Prace Naukowe Akademii im. Jana Długosza w CzęstochowieSearch in Google Scholar

‘Zastosowanie metod ekonometryczno- statystycznych w zarządzaniu finansami zakładów ubezpieczeń’ in: W Ronka Chmielowiec (ed) (Wydawnictwo AE, Wrocławiu 2004)‘Zastosowanie metod ekonometryczno- statystycznych w zarządzaniu finansami zakładów ubezpieczeń’in:ChmielowiecW Ronka(ed)Wydawnictwo AEWrocławiu2004Search in Google Scholar

J Zawal, ‘Edukacja kobiet wczoraj i dziś’ (2006) 4 Edukacja DorosłychZawalJ‘Edukacja kobiet wczoraj i dziś’20064Edukacja DorosłychSearch in Google Scholar

Articoli consigliati da Trend MD

Pianifica la tua conferenza remota con Sciendo