An Analysis of Field Preferences of an Educational System

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).

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.

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).

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.

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).

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).

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 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.

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).

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).

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.

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: (5)mi=1−di0d0,(i=1,2,…n),{m_i} = 1 - {{{d_{i0}}} \over {d0}},(i = 1,2, \ldots n), where m_i 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.

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).

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.

Figure 1

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.

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.

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).

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

Figure 2

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

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.

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).