|
Snyder’s Self-Monitoring Scale: short version, reliability, validity, factor structure
DOI: 10.23951/2782-2575-2024-1-62-74
The widespread use of smartphones and social networks has profoundly affected the quality of communication and led to changes in its characteristics, including those measured by the Self-Monitoring Scale. Examining these changes is crucial, especially in the context of the evolving landscape of digital communication. Although the Self-Monitoring Scale was introduced 40 years ago by M. Snyder, it remains a staple of research, demonstrating its enduring applicability. However, there is a growing consensus in the psychological community, both domestically and internationally, that the established methods need to be updated. The reason for this is the potential discrepancy between the responses of today’s respondents and those of people from decades past for whom these scales were developed initially. The changing communication context requires re-evaluating these tools to ensure that they remain relevant and reflect current societal dynamics. The aim of the study to reduce the number of questions in the Scale is also important, as large questionnaires cause difficulties in collecting material and (as relevant offline and online studies have shown) lead to poorer quality responses. Aims of the research: 1) Development of a reliable and valid short version of the Self-Monitoring Scale by M. Snyder; 2) Construction of meaningful models for the Self-Monitoring Scale. The empirical basis of the study was the results of online tests with 1911 respondents from Belarus and Russia, including 1206 women and 605 men. The study was based on the classic test by M. Snyder Self-Monitoring Scale, questionnaires on smartphone addiction (author – V.P. Sheinov), addiction to social networks (authors – V.P. Sheinov, A.S. Dziavitsyn) and the Academic Motivation Scale questionnaire by Vallerand (adapted to the Russian-speaking society by T.O. Gordeeva, O.A. Sychev and E.N. Osin) was also used. Statistical analysis was performed using the SPSS-22 package and the R-based Jamovi version 2.3.21. As a result of this study, a valid and reliable short version of the Self-Monitoring Scale questionnaire was created, consisting of 8 questions on self-monitoring, with better psychometric properties than the original version created by M. Snyder. A rich two-factor model of the Self-Monitoring Scale was developed. The short Self-Monitoring Scale allows you to collect larger samples with better-quality responses.
Ключевые слова: Self-Monitoring Scale, M. Snyder, short version of the Self-Monitoring Scale, reliability, validity, factor structure, psychometric properties, smartphone addiction, social media addiction, academic motivation scales
Библиография:
1. Sheinov V.P. Svyazi zavisimosti ot smartfona s sostoyaniyami i svoystvami lichnosti [Connections between smartphone addiction and personality states and properties]. Zhurnal Belorusskogo gosudarstvennogo universiteta. Sotsiologiya – Journal of the Belarusian State University. Sociology. 2020;(4):120-127 (in Russian).
2. Sheinov V.P. Vzaimosviyazi zavisimosti ot smartfona s psikhologicheskimi i sotsial’nopsikhologicheskimi kharakteristikami lichnosti obzor zarubezhnykh issledovanii [Relationships between smartphone addiction and psychological and socio-psychological personality characteristics: a review of foreign studies]. Vestnik Rossijskogo universiteta druzhby narodov. Seriya: Psihologiya i pedagogika – Bulletin of the Russian Peoples’ Friendship University. Series: Psychology and Pedagogy. 2021; 18(1):235-253 (in Russian). doi: 10.22363/2313-1683-2021-18-1-235-253
3. Sheinov V.P. Zavisimost’ ot social’nykh setey i kharakteristiki lichnosti: obzor issledovaniy [Social media addiction and personality characteristics: a review of research]. Vestnik Rossiyskogo universiteta druzhby narodov. Seriya: Psykhologiya i pedagogika – Bulletin of the Russian Peoples’ Friendship University. Series: Psychology and Pedagogy. 2021;18(3):607-630.
4. Sheinov V.P. Vnutrilichnostnye prediktory viktimizatsii [Intrapersonal predictors of victimization]. Institut psikhologii Rossiyskoy akademii nauk. Organizatsionnaya psikhologiya i psikhologiya truda – Institute of Psychology of the Russian Academy of Sciences. Organizational and occupational psychology. 2019;4(1):154-182.
5. Snyder M. Self-monitoring of expressive behavior. Journal of Personality and Social Psychology. 1974;IN.30:526-537.
6. Snyder M., Gangestad S. On the nature of self-monitoring: Matters of assessment, matters of validity. Journal of personality and social psychology. 1986;IN.51(1):125-139. doi:10.1037/0022-3514.51.1.125
7. Herzog A.R., Bachman J.G. Effects of Questionnaire Length on Response Quality. The Public Opinion Quarterly. 1981;45(4):549-559. http://www.jstor.org/stable/2748903
8. Galesic M., Bosnjak M. Effects of questionnaire length on participation and indicators of response quality in a web survey. Public Opinion Quarterly. 2009;IN.73(2):349-360
9. Raygorodskiy D.Ya. (editor-compiler) Prakticheskaya psikhodiagnostika. Metodiki i testy [Practical psychodiagnostics. Methods and tests]. Samara, Bahrah-M Publ., 2022: 672 p. (in Russian).
10. Sheinov V.P. Adaptatsiya i validizatsiya oprosnika “Shkala zavisimosti ot smartfona” dlya russkoyazychnogo sotsiuma [Adaptation and validation of the questionnaire “Smartphone addiction scale” for the Russianspeaking society]. Sistemnaya psihologiya i sotsiologiya – System Psychology and Sociology, 2020, no. 3 (35), pp. 75–84 (in Russian).
11. Sheynov V.P., Devitsyn A.S. Razrabotka nadezhnogo i validnogo oprosnika zavisimosti ot sotsialnykh setei [Development of a Reliable and Valid Social Network Addiction Questionnaire]. Sistemnaya psikhologiya i sotsiologiya – Systems Psychology and Sociology. 2021;(2):41-55 (in Russian). doi: 10.25688/2223-6872.2021.38.2.04 (accessed 23 January 2023).
12. Gordeeva T.O., Sychev O.A., Osin E.N. Oprosnik “SHkaly akademicheskoj motivatii” [Questionnaire “Academic Motivation Scales”]. Psykhologicheskiy zhurnal – Psychological Journal. 2014;35(4):96-107 (in Russian).
13. Briggs S.R., Cheek J.M., Buss A.H. An analysis of the Self-Monitoring Scale. Journal of Personality and Social Psychology. 1980;38:679-686.
14. Snyder M., Gangestad S. Self-monitoring: Appraisal and reappraisal. Psychological Bulletin. 2000;126(4):530-555.
15. Kim J., Hahn K.H. The effects of self-monitoring tendency on young adult consumers’ mobile dependency. Computers in Human Behavior. 2015;IN.50:169-176.
16. Abhari K., Klase M., Koobchehr F., Olivares F., Pesavento M., Sosa L., Vaghefi I. Toward a theory of digital mindfulness: A case of smartphone-based self-monitoring. International Conference on Human-Computer Interaction. 2021:549–561.
17. Shen E., Shen J., Chia T.L. Development of an app to support self-monitoring smartphone usage and healthcare behaviors in daily life. In: Proceedings of the 3rd International Conference on Big Data and Internet of Things. 2019:29-34.
18. Abhari K., Vaghefi I. Screen time and productivity: an extension of goal-setting theory to explain optimum smartphone use. AIS Transactions on Human-Computer Interaction. 2022;IN.14(3):254-288.
19. Sahranç Ü. A Study on the Relationship Between Social Media Addiction and Self-Regulation Processes among University Students. International Journal of Psychology and Educational Studies. 2021;IN.8(4):96-109.
20. Pornsakulvanich V. Excessive use of Facebook: The influence of self-monitoring and Facebook usage on social support. Kasetsart Journal of Social Sciences. 2018;IN.39(1): 116-121
21. Dogan H., Norman H., Alrobai A., Jiang N., Nordin N., Adnan A. A web-based intervention for social media addiction disorder management in higher education: Quantitative survey study. Journal of medical Internet research. 2019;IN.21(10): e14834
22. Kleinbaum A.M., Jordan A.H., Audia P.G. An altercentric perspective on the origins of brokerage in social networks: How perceived empathy moderates the self-monitoring effect. Organization Science. 2015;IN.26(4):1226-1242.
23. Varnali K., Toker A. Self-disclosure on social networking sites. Social Behavior and Personality: an international journal. 2015;IN.43(1):1-13.
24. Zhu M., Doo M.Y. The relationship among motivation, self-monitoring, self-management, and learning strategies of MOOC learners. Journal of Computing in Higher Education. 2022:1-22.
25. Chang M.M. Effects of self-monitoring on web-based language learner’s performance and motivation. Calico Journal. 2010;IN.27(2):298-310.
26. Chang M.M. Enhancing web‐based language learning through self‐monitoring. Journal of Computer Assisted Learning. 2007;IN.23(3):187-196.
27. Zhu M., Bonk C.J., Doo M.Y. Self-directed learning in MOOCs: Exploring the relationships among motivation, self-monitoring, and self-management. Educational Technology Research and Development. 2020;IN.68:2073-2093.
28. Kanani Z., Adibsereshki N., Haghgoo H.A. The effect of self-monitoring training on the achievement motivation of students with dyslexia. Journal of Research in Childhood Education. 2017;IN.31(3):430–439.
( 489.26 kB ) 
( 479.94 kB )
Выпуск: 1, 2024
Серия выпуска: Issue 1
Рубрика:
Страницы: 62 — 74
Скачиваний: 248