Open AccessAnalysis of Statistical Methods on Plurilinguistic Quantitative Data
Author(s) -
T.O. Krasnopeeva,
M. Gromov
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1611/1/012058
Subject(s) - psychology , phenomenon , rasch model , competence (human resources) , multidisciplinary approach , set (abstract data type) , nomothetic and idiographic , mathematics education , social psychology , computer science , developmental psychology , sociology , epistemology , social science , philosophy , programming language
This multidisciplinary study deals with a population cluster of migrant multilinguals as a complex phenomenon of Social Physics with the help of the mathematical-statistical tools. It enlightens two statistical methods used to analyse the quantitative data on the plurilingual competence of the university students: the Rasch analysis and the T-test. These methods are able to give a more profound picture of the individual migrants’ plurilingual ability in terms of migration as a sociophysical phenomenon. We consider the strengths that each method has as the physical values in order to understand the linguistic behavior of migrant flow as a complex matter and each migrant student as a part of this flow. Our multidisciplinary study empirically investigates whether the positive impact of the statistical methods is relevant by using recent dataset on the example of the multilinguals’ social cluster. The main conclusion of the study shows the positive impact of the Rasch model in measurement of complex sociophysical matters with respect to the ability of a migrant social cluster to the plurilingualism, and results in the statement that this tool is capable to decipher the empirical data in a qualitative manner, in particular, for its application to the group educational trajectories. However, T-test showed the statistical insignificance of the current data set if this data set is used to predict further development of plurilingual competence.