Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M)
ISSN (print): 1994-8964, ISSN (online): 2949-463Х
Since 2023, has been published in the online format
Publication frequency: 2 times per year. Founded in 1991
Editor-in-Chief - Nikolay S. Babich, PhD in Sociology.
Indexation: WoS RSCI, RSCI, VAK RF
Double blind peer review
Open Access
The orientation of the journal makes it unique specialized publication focusing on the methodology and methods of sociological research, the collection, measurement and analysis of social science data, building mathematical models of social processes.
Main Columns:
- Social Research Methodology
- Theory and History of methods
- Data collection procedures
- Measurement techniques
- Mathematical modeling
- Methods of data analysis
- Selective strategies
- Information and software research
- Teaching methodology and methods of sociological research
- Consulting
Current Issue
No 59 (2024)
Methodology of sociological research
“Thomas theorem” and self-fulfilling prophecy: methodological analysis of concepts
Abstract
The article proposes an analysis of using «Thomas theorem» in sociological discourse against the background of the widespread belief that this «theorem» is a synonym for «self-fulfilling prophecy». The author proves that these principles describe two partially intersecting sets of situations. The main conclusion is that only some self-fulfilling prophecies are explained by «Thomas theorem» and only some cases of applicability of the theorem relate to self-fulfilling prophecies. Apparently, W.I. Thomas and R.Merton understood this terminological nuance, but then the understanding was lost. This loss obscured the meaning of Thomas's theorem. The author also analyzes the methodological value of correctly understood Thomas theorem, which consists in the possibility to incorporate human subjectivity into causally interpreted reality. This allows classifying social ontologies in Thomasian and non-Thomasian.
Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M). 2024;(59):7-30
7-30
Epractices of data collection and analysis
Leveraging social media big data to analyze internal migration
Abstract
The article is devoted to the development of a methodology for the study of population migration based on the analysis of big data in social networks through thesearch for patterns showing the internal migration process in the messages of social media users. The research in question allowed us to evaluate the degree of validity and relevance of digital traces of individuals as a source of empirical data on internal migration.A software solution in the form of the Brand Analytics platform was used to generate the initial empirical data base onsisting of relocation messages published by social media users. The approbation of the methodology showed the fragmentation of research, analytical and predictive possibilities of using social networks as a source of data on intra-Russian population migration. Narrative analysis based on the formed sample of messages of users who have experience of resettlement within the country was proposed for further development of similar types of research.
Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M). 2024;(59):31-55
31-55
Developing a procedure for secondary data analysis on foreign attitudes toward Russia: a case of Japan
Abstract
The attention of readers is drawn to a presentation of the principles of collecting and analyzing secondary data on the attitude towards Russia of public opinion in foreign countries, based on a generalization of the experience of similar studies. Each principle cited is illustrated by the example of a study of the attitude towards Russia of the Japanese.It is assumed that the methodology of secondary analysis should provide information that meets the following requirements: a) reliable and valid; b) with as unambiguous and clear interpretation as possible; c) having predictive value. To ensure this, it is necessary to take into account that 1) a meaningful interpretation of the dynamics of public opinion requires taking into account its historical state and, accordingly, a long analyzed period; 2) "attitude towards Russia" or any other country is a complex, heterogeneous and multidimensional latent variable, therefore, for secondary analysis, it is desirable to include as many empirical indicators as possible that have appeared in various mass surveys; 3) at the same time, the "attitude towards Russia" should be considered as a single variable, which is impossible without the reduction of a multitude of indicators, which is ensured, at least, by their homogeneity.The proposed conditions, applied to representative mass surveys, face the fact that their data usually have rigid restrictions both in the number of indicators and in the time coverage, which gives rise to the inevitable fragmentation of the data. Therefore, the collected information can, as a rule, be reduced primarily to some generalized "qualitative" assessments of the situation. These can then be formally combined into indices (for example, an index of favorability) for generalization and interpretation. At the level of indices, there are opportunities for partial and conditional filling of gaps in fragmented data.The application of the described principles and techniques to the public opinion of Japan made it possible to conclude that at the present time, the attitudes towards Russia in the public opinion of Japan are experiencing the deepest crisis in the entire post-Soviet history, and there are all the grounds to call the sentiments of Japanese public opinion clearly anti-Russian. Obtaining this conclusion did not require independent collection of empirical data, all the necessary survey results were already available in the public domain, their volume and quality allowed for an analysis that seems quite reliable and valid.
Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M). 2024;(59):56-91
56-91
Analytical reviews
Russian-language scales of religiosity: analytical review
Abstract
The concept of "religiosity" is firmly entrenched in the mechanisms of social identification and differentiation. In international research practice, the problem of generalizing and systematizing methods for measuring religiosity was first solved by P. Hill and R. Hood. To apply the scales of religiosity in Russian practice, they need to be tested on a Russian sample. Practically oriented reviews of scales are almost absent. This article was written to make up for this shortcoming.During the research, criteria were formulated for the selection of scales by relevance. The author managed to find only four methods for measuring religiosity that meet all five criteria: the scale "predisposition to religiosity," CRS, a questionnaire of spiritual experiences, RCI-10. Further, the criteria for ordering and evaluation were formulated. The work carried out showed the existence of each tool of its own advantages and disadvantages. The scale of religious predisposition has the undoubted advantage of brevity, ease of application, understanding by respondents and transparency of interpretation. But this technique needs significant refinement and full testing. Of the remaining three methods, the Russian-language version of the CRS scale is the most widespread. Apparently, it is this scale that should be recommended as a method for measuring the religiosity of the "first choice." The scales of KSSh-10 and spiritual experiences are also tested to high standards in the Russian sample and have sufficient methodological qualities, but their application is advisable when the study has a certain thematic specificity.
Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M). 2024;(59):92-116
92-116
Translations
117-155
Reviews
Myagkov A. Yu. Indirect methods in sensitive research (in Russian). Moscow: INFRA-M, 2024. 245 p. ISBN 978-5-16-019344-1. DOI: 10.12737/2110854.
Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M). 2024;(59):156-161
156-161
Scientific life
Scientific life
Sociology : Methodology, Methods, Mathematical Modeling (Sociology: 4M). 2024;(59):162-170
162-170