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Adolescents’ risk behavior and school climate: aggression and alcohol consumption

Priority areas of development: sociology
2018
The project has been carried out as part of the HSE Program of Fundamental Studies.

Goal of research was to conduct analytical and methodical work for the study of two main aspects of adolescents’ risk behavior – aggression and alcohol consumption – with regard to the contexts of different levels: familial characteristics, and features of educational institutions and classes.

We mainly applied the quantitative data analysis in this project. We used such methods as factor analysis, multiple logistic and multilevel regressions, spatial modeling, structural equation modeling, item response theory, multiple imputation for longitudinal network data, as well as some special tools design for data structure comparison, such as segregation indexes.

Empirical base of the project contains the data gathered by the Laboratory through the last years: (1) 27 608 students from 247 schools of Kaluga region – 2016-2017; (2) 9000 students from 237 schools in the localities of different types (St.Petersburg and Leningrad region) – 2009-2014; (3) 1450 students of technical colleges in St.Petersburg (waves 3-4 of the longitudinal project on adolescents’ health and risk behavior) – 2017-18.

Results of research

For the part of the project dedicated to the development of new research methods we tested our original complex diagnostics of the school climate. We added new scales on students’ and teachers’ assessment of school climate; all of scales got tested on the data of different types and pupils of different ages. We also analyzed three mechanisms of missingness in longitudinal network data, and evaluated the performance of the multiple imputation procedure based on stationary SAOM for each of them. As a result, we developed a method of treatment of the missings in longitudinal network data. School differentiation is viewed in the study as an important context of climate formation; we developed a method measuring the differentiation between and within schools, which allows capturing the differences between instances based on various characteristics.

Empirical data analysis allowed us to achieve the following results

A. School climate. We demonstrated the dynamics of educational motivation and discovered the factors of educational environment of technical colleges, which are related to motivation’s rise and decline. In particular, we showed that the level of educational motivation at the end of the second year depends on a set of students’ individual characteristics on the one hand, and the support from the teachers and masters (career tutors) – on the other hand.

We conducted an analysis of different components of the school climate, and its relation to other characteristics of the school. The report presents the results of school climate evaluation with regard to socio-economic composition of the group, school’s type and size. We demonstrate: (1) how socio-psychological characteristics of the students differ by age; (2) what changes occur in schools in one year; (3) how the attitudes of and relations between the students of different ages change.

Intraschool and interschool differentiation was measured with the indexes of Theil and Duncan. This way we tested the possible grounds of school differentiation: socio-economic status of student’s family, mother’s higher education, student’s educational aspirations and academic achievements. For these features, we haven’t found any schools or localities with critical or high level of differentiation. In the urban cases of both large and small scale, the differentiation by educational aspirations appears to be low between classes (within schools), but it is much higher between schools. The schools of St.Petersburg are differentiated by maternal education and – to a lesser extent – by socio-economic status; the intraschool selection on these two grounds appears to be higher.

B. Aggression and risk behavior. We studied the specific traits of bullying and victimization in the Russian context, and conducted the comparison of the characteristics of bullying participants of the four types. In particular, aggressors and victims appear to have some common characteristics (like low performance). We prove that there is statistical relationship between aggressor’s sociometric status and the context on the class level.

We prove that adolescents’ alcohol consumption and smoking behavior do not become grounds for their popularity or marginalization, but it does affect friendship ties formation. The effects of influence of the environment of risk behavior differ by the type of risk behavior and the gender of those involved. We demonstrate that parental control decreases the risk of alcohol consumption by adolescents.

Level of implementation, recommendations on implementation or outcomes of the implementation of the results

The developed methodology of school differentiation measurement allows assessing the level of differentiation by various criteria and compare the results from diverse localities. This gives us an opportunity to develop and implement some specific measures in the field of educational policy, which will be adequate to the local context.

Based on the results of the studies, Laboratory continues sending the reports to all the participating educational institutions. These reports contain the information on socio-psychological characteristics of the students, their professional involvement, risk behavior, and its change (on the level of educational institution and on the level of the study groups), holding the anonymity conditions.

The results of the study of risk behavior might be of interest for educational institutions (schools and technical colleges), as well as for the representatives of the education committee. In the future they can be used as a basis for the development of recommendations in  the field of social policy on the prevention of risk behavior.

Publications:


Daniel Alexandrov, Karepin V., Musabirov I., Daria Chuprina. Educational Migration from Russia to the Nordic Countries, China and the Middle East. Social Media Data, in: Companion Proceedings of the The Web Conference 2018. Geneva : International World Wide Web Conferences Steering Committee, 2018. P. 49-50. doi
Musabirov I., Bakhitova A. Code-sharing networks of non-STEM students: the case of data science minor, in: Proceedings of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education. , 2018. P. 389-389. doi
Баранова В. В. Как возникают новые формы отрицания в калмыцком языке // Урало-алтайские исследования. 2018. Т. 2. № 29. С. 7-17.
Федорова К. С., Baranova V. V. Moscow: Diversity in disguise, in: Urban Sociolinguistics: The City as a Linguistic Process and Experience. L., NY : Routledge, 2018. Ch. 14. P. 220-236. doi
Ivaniushina V. A., Alexandrov D. A. Anti-school attitudes, school culture and friendship networks // British Journal of Sociology of Education. 2018. Vol. 39. No. 5. P. 698-716. doi
Александров Д. А., Воскресенский В. М., Савельева С. С. Роль внеклассной активности в формировании социального неравенства: случай малого города // В кн.: Образование и социальная дифференциация: коллективная монография / Отв. ред.: М. Карной, И. Д. Фрумин, Н. Н. Кармаева. М. : Издательский дом НИУ ВШЭ, 2017. Гл. 4.2. С. 388-416.