THE FACTORS OF STUDENTS’ AGGRESSIVENESS IN ADOLESCENCE AND YOUTH

The article is devoted to the study of the actual problem of the aggressiveness of students within the walls of educational institutions, as evidenced by the growing statistics of tragic incidents in Russian schools and colleges in recent years. The subject of the research is the personal factors students’ aggressiveness from senior classes. The purpose of the research is a comparative study of personal factors, the focus of aggressiveness and the level of a conflictness of students in adolescence and youth.A theoretical basis of the study is classical approaches to the understanding of aggressiveness as a personal construct of factors that determine readiness for aggressive manifestations of negative and positive orientation. For an empirical study 158 adolescents and young people (14–17 years old) were selected.The method of organizing the investigation is an ascertaining experiment. The empirical data collection was carried out using the method of “Personal aggressiveness andconflictness” (E.P. Ilyin P.A. Kovalev).The results obtained allowed us to give a comparative description of the personal factors of aggressiveness, its focus and students’ conflictness levels in adolescence and youth. On the basis of the assumptions put forward about interiorization and socialization as two mechanisms for the development of aggressiveness, the author identified the most important aspects of psychological and educational prevention of aggressive behavior.

Method versatility in analysing human attitudes towards technology

Various research domains are facing new challenges brought about by growing volumes of data. To make optimal use of them, and to increase the reproducibility of research findings, method versatility is required. Method versatility is the ability to flexibly apply widely varying data analytic methods depending on the study goal and the dataset characteristics. Method versatility is an essential characteristic of data science, but in other areas of research, such as educational science or psychology, its importance is yet to be fully accepted. Versatile methods can enrich the repertoire of specialists who validate psychometric instruments, conduct data analysis of large-scale educational surveys, and communicate their findings to the academic community, which corresponds to three stages of the research cycle: measurement, research per se, and communication. In this thesis, studies related to these stages have a common theme of human attitudes towards technology, as this topic becomes vitally important in our age of ever-increasing digitization. The thesis is based on four studies, in which method versatility is introduced in four different ways: the consecutive use of methods, the toolbox choice, the simultaneous use, and the range extension. In the first study, different methods of psychometric analysis are used consecutively to reassess psychometric properties of a recently developed scale measuring affinity for technology interaction. In the second, the random forest algorithm and hierarchical linear modeling, as tools from machine learning and statistical toolboxes, are applied to data analysis of a large-scale educational survey related to students’ attitudes to information and communication technology. In the third, the challenge of selecting the number of clusters in model-based clustering is addressed by the simultaneous use of model fit, cluster separation, and the stability of partition criteria, so that generalizable separable clusters can be selected in the data related to teachers’ attitudes towards technology. The fourth reports the development and evaluation of a scholarly knowledge graph-powered dashboard aimed at extending the range of scholarly communication means. The findings of the thesis can be helpful for increasing method versatility in various research areas. They can also facilitate methodological advancement of academic training in data analysis and aid further development of scholarly communication in accordance with open science principles.Verschiedene Forschungsbereiche müssen sich durch steigende Datenmengen neuen Herausforderungen stellen. Der Umgang damit erfordert – auch in Hinblick auf die Reproduzierbarkeit von Forschungsergebnissen – Methodenvielfalt. Methodenvielfalt ist die Fähigkeit umfangreiche Analysemethoden unter Berücksichtigung von angestrebten Studienzielen und gegebenen Eigenschaften der Datensätze flexible anzuwenden. Methodenvielfalt ist ein essentieller Bestandteil der Datenwissenschaft, der aber in seinem Umfang in verschiedenen Forschungsbereichen wie z. B. den Bildungswissenschaften oder der Psychologie noch nicht erfasst wird. Methodenvielfalt erweitert die Fachkenntnisse von Wissenschaftlern, die psychometrische Instrumente validieren, Datenanalysen von groß angelegten Umfragen im Bildungsbereich durchführen und ihre Ergebnisse im akademischen Kontext präsentieren. Das entspricht den drei Phasen eines Forschungszyklus: Messung, Forschung per se und Kommunikation. In dieser Doktorarbeit werden Studien, die sich auf diese Phasen konzentrieren, durch das gemeinsame Thema der Einstellung zu Technologien verbunden. Dieses Thema ist im Zeitalter zunehmender Digitalisierung von entscheidender Bedeutung. Die Doktorarbeit basiert auf vier Studien, die Methodenvielfalt auf vier verschiedenen Arten vorstellt: die konsekutive Anwendung von Methoden, die Toolbox-Auswahl, die simultane Anwendung von Methoden sowie die Erweiterung der Bandbreite. In der ersten Studie werden verschiedene psychometrische Analysemethoden konsekutiv angewandt, um die psychometrischen Eigenschaften einer entwickelten Skala zur Messung der Affinität von Interaktion mit Technologien zu überprüfen. In der zweiten Studie werden der Random-Forest-Algorithmus und die hierarchische lineare Modellierung als Methoden des Machine Learnings und der Statistik zur Datenanalyse einer groß angelegten Umfrage über die Einstellung von Schülern zur Informations- und Kommunikationstechnologie herangezogen. In der dritten Studie wird die Auswahl der Anzahl von Clustern im modellbasierten Clustering bei gleichzeitiger Verwendung von Kriterien für die Modellanpassung, der Clustertrennung und der Stabilität beleuchtet, so dass generalisierbare trennbare Cluster in den Daten zu den Einstellungen von Lehrern zu Technologien ausgewählt werden können. Die vierte Studie berichtet über die Entwicklung und Evaluierung eines wissenschaftlichen wissensgraphbasierten Dashboards, das die Bandbreite wissenschaftlicher Kommunikationsmittel erweitert. Die Ergebnisse der Doktorarbeit tragen dazu bei, die Anwendung von vielfältigen Methoden in verschiedenen Forschungsbereichen zu erhöhen. Außerdem fördern sie die methodische Ausbildung in der Datenanalyse und unterstützen die Weiterentwicklung der wissenschaftlichen Kommunikation im Rahmen von Open Science

Depression, anxiety, and burnout in academia: topic modeling of PubMed abstracts

The problem of mental health in academia is increasingly discussed in literature, and to extract meaningful insights from the growing amount of scientific publications, text mining approaches are used. In this study, BERTopic, an advanced method of topic modeling, was applied to abstracts of 2,846 PubMed articles on depression, anxiety, and burnout in academia published in years 1975–2023. BERTopic is a modular technique comprising a text embedding method, a dimensionality reduction procedure, a clustering algorithm, and a weighing scheme for topic representation. A model was selected based on the proportion of outliers, the topic interpretability considerations, topic coherence and topic diversity metrics, and the inevitable subjectivity of the criteria was discussed. The selected model with 27 topics was explored and visualized. The topics evolved differently with time: research papers on students' pandemic-related anxiety and medical residents' burnout peaked in recent years, while publications on psychometric research or internet-related problems are yet to be presented more amply. The study demonstrates the use of BERTopic for analyzing literature on mental health in academia and sheds light on areas in the field to be addressed by further research

Combining statistical and machine learning methods to explore German students’ attitudes towards ICT in PISA

In our age of big data and growing computational power, versatility in data analysis is important. This study presents a flexible way to combine statistics and machine learning for data analysis of a large-scale educational survey. The authors used statistical and machine learning methods to explore German students’ attitudes towards information and communication technology (ICT) in relation to mathematical and scientific literacy measured by the Programme for International Student Assessment (PISA) in 2015 and 2018. Implementations of the random forest (RF) algorithm were applied to impute missing data and to predict students’ proficiency levels in mathematics and science. Hierarchical linear models (HLM) were built to explore relationships between attitudes towards ICT and mathematical and scientific literacy with the focus on the nested structure of the data. ICT autonomy was an important variable in RF models, and associations between this attitude and literacy scores in HLM were significant and positive, while for other ICT attitudes the associations were negative (ICT in social interaction) or non-significant (ICT competence and ICT interest). The need for further research on ICT autonomy is discussed, and benefits of combining statistical and machine learning approaches are outlined

Latent Class Cluster Analysis: Selecting the number of clusters

Latent Class Cluster Analysis (LCCA) is an advanced model-based clustering method, which is increasingly used in social, psychological, and educational research. Selecting the number of clusters in LCCA is a challenging task involving inevitable subjectivity of analytical choices. Researchers often rely excessively on fit indices, as model fit is the main selection criterion in model-based clustering; it was shown, however, that a wider spectrum of criteria needs to be taken into account. In this paper, we suggest an extended analytical strategy for selecting the number of clusters in LCCA based on model fit, cluster separation, and stability of partitions. The suggested procedure is illustrated on simulated data and a real world dataset from the International Computer and Information Literacy Study (ICILS) 2018. For the latter, we provide an example of end-to-end LCCA including data preprocessing. The researcher can use our R script to conduct LCCA in a few easily reproducible steps, or implement the strategy with any other software suitable for clustering. We show that the extended strategy, in comparison to fit indices-based strategy, facilitates the selection of more stable and well-separated clusters in the data. • The suggested strategy aids researchers to select the number of clusters in LCCA • It is based on model fit, cluster separation, and stability of partitions • The strategy is useful for finding separable generalizable clusters in the data

MACHIAVELLIANISM AND PERSONAL TRAITS IN YOUNG AGE

The article is devoted to one of the vital problems of modern society, the problem of Machiavellianism.  It is noted that manipulative forms of interaction between people become common, and it can lead to a tendency to use the manipulation by an increasing number of people and, in its turn, to the loss of openness, sincerity, and congruence of communication.The results of an empirical study of Machiavellianism and its interrelationship with personality traits in young age are discussed. The study is based on a dispositional approach to the person, suggesting that it consists of reliable, stable, interrelated elements (properties, traits) that determine its internal essence and behavior. Differences in the behavior of people are explained by the differences in the intensity of personality traits. The article shows that adolescence is one of the most sensitive ways to consolidate manipulative strategies in the behavior of a person. The obtained results prove the existence of a interrelationship between Machiavellianism and a number of personality traits, according to the theory of R.B. Cattell. Among such personality traits are communicative (closed), regulative (low normative, low self-control behavior), emotional (rigidity) and intellectual (radicalism). Knowledge of the personal profile of a personality prone to Machiavellianism in adolescence will allow continuing the study within the framework of studying ways of preventing extreme manifestations of manipulative behavior and widening the range of constructive patterns of youth behavior

A multi-method psychometric assessment of the affinity for technology interaction (ATI) scale

In order to develop valid and reliable instruments, psychometric validation should be conducted as an iterative process that “requires a multi-method assessment” (Schimmack, 2019, p. 4). In this study, a multi-method psychometric approach was applied to a recently developed and validated scale, the Affinity for Technology Interaction (ATI) scale (Franke, Attig, & Wessel, 2018). The dataset (N ​= ​240) shared by the authors of the scale (Franke et al., 2018) was used. Construct validity of the ATI was explored by means of hierarchical clustering on variables, and its psychometric properties were analysed in accordance with an extended psychometric protocol (Dima, 2018) by methods of Classical Test Theory (CTT) and Item Response Theory (IRT). The results showed that the ATI is a unidimensional scale (homogeneity H ​= ​0.55) with excellent reliability (ω ​= ​0.90 [0.88-0.92]) and construct validity. Suggestions for further improvement of the ATI scale and the psychometric protocol were made

Income stratification in Russia: what do different approaches demonstrate?

This paper provides empirical analysis of income stratification in contemporary Russian society and its dynamics in recent decades. The paper analyses in detail different approaches (absolute and relative) to defining income groups. It is shown that the most widely used thresholds of the absolute approach cannot be efficiently applied to contemporary Russian society, as they fail to define the subgroups within the population, while relative approach, based on the median income as the social standard of living, appears more effective for income stratification in Russia. A specific income stratification scale is suggested. Its application shows that middle-income groups currently dominate in income structure, however, the incomes of their representatives are not high in absolute terms and their living standards are quite modest. Income stratification in Russia has been noticeably transformed over the last 20 years - the middle-income group has been growing while the low income and high-income groups' shares have been declining. The proposed scale implies possibilities for structural adjustments such as regional- and settlement-specific disparities in income distribution; it can be easily replicated and allows broad potential for future research, including international comparisons of income stratification in societies undergoing transitional processes