RAS Social ScienceСоциологические исследования Sotsialogicheski issledovania

  • ISSN (Print) 0132-1625
  • ISSN (Online) 3034-6010

DEMOGRAPHIC CORRELATES OF ATTRITION IN LONGITUDINAL ONLINE SURVEYS IN RUSSIA: EVIDENCE FROM FOUR WAVES OF THE "VALUES IN CRISIS" PROJECT

PII
S30346010S0132162525090046-1
DOI
10.7868/S3034601025090046
Publication type
Article
Status
Published
Authors
Volume/ Edition
Volume / Issue number 9
Pages
34-48
Abstract
This study explores the demographic correlates of attrition in longitudinal online surveys by utilizing data from four Russian waves of the international «Values in Crisis» project (designed to examine societal consequences of the COVID-19 pandemic). Respondents were recruited from an online consumer panel, maintained by OMI, a leading Russian marketing research company. Data collection occurred in June 2020, April-May 2021, November-December 2021, and July-September 2022. Only 606 (39.7%) out of 1527 initial participants took part in all four rounds; the rest missed at least one round or dropped out. Both descriptive statistics and binary logistic regression analysis reveal that older, male, wealthier, and more educated participants had a higher probability of completing all four rounds. To sum up, attrition in online panels in the Russian context can be substantial and is largely non-random in demographic terms. Researchers should take this into account when planning longitudinal web surveys and interpreting their results.
Keywords
онлайн-опрос лонгитюдное исследование истощение выборки
Date of publication
09.12.2025
Year of publication
2025
Number of purchasers
0
Views
31

References

  1. 1. Афанасьева Ю. А., Соколов Б. О., Широканов А. А. Изменчивость ковид-скептических установок в России: результаты анализа двух волн лонгитюдного опроса «Ценности в кризисе» // Мониторинг общественного мнения: экономические и социальные перемены. 2024. № 2. С. 53–77. DOI: 10.14515/monitoring.2024.2.2523. @@Afanasyeva Y. A., Sokolov B. O., Shirokanova A. A. (2024) Variability of COVID Skeptical Attitudes in Russia: Findings from Two Waves of the "Values in Crisis" Longitudinal Survey. Monitoring obshthestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny [Monitoring of Public Opinion: Economic and Social Changes]. No. 2: 53–77. DOI: 10.14515/monitoring.2024.2.2523. (In Russ.)
  2. 2. Гаврилов К. А. Платформа Толока как источник респондентов для онлайн-опроса: опыт оценки качества данных // Социология: 4М. 2022. № 53. С. 165–209. DOI: 10.19181/dm.2021.53.5. @@Gavrilov K. A. (2022) Toloka Platform as a Source of Online Survey Participants: An Experience of Assessing Data Quality. Sociologia: 4M [Sociology: Methodology, Methods, Mathematical Modeling (Sociology: 4M)]. No. 53: 165–209. DOI: 10.19181/4m.2021.53.5. (In Russ.)
  3. 3. Девятко И. Ф. Онлайн исследования и методология социальных наук: новые горизонты, новые (и не столь новые) трудности // Онлайн исследования в России 2.0 / Под ред. А. В. Шашкина, И. Ф. Девятко, С. Г. Давыдов. М.: ОМI RUSSIA, 2010. С. 17–30. @@Devyatko I. F. (2010) Online Research and Methodology of Social Sciences: New Horizons, New (and Not So New) Challenges. In: Shashkin A. V., Devyatko I. F., Davydova S. G. (eds) Online Research in Russia. 2.0. Moscow: OMI RUSSIA: 17–30. URL: https://omirussia.ru/knowledge/books/12/ (accessed 01.02.2025). (In Russ.)
  4. 4. Девятко И. Ф. Методы социологического исследования. Екатеринбург: Уральский ун-т, 1998. @@Deviatko I. F. (1998) Methods of Sociological Research. Yekaterinburg: Uralskii un-t. (In Russ.)
  5. 5. Корсунова В. И., Понарин Э. Д. и др. Values in Crisis – International / Ценности в кризис (данные по российской выборке) 2020–2022. 2024. Номер свидетельства: RU 2024621301. Дата регистрации: 27.03.2024. URL: http://www1.fips.ru/fips_servi/fips_service? DB=DB&DocNumber=20246213018TypeFile=html? (дата обращения: 25.01.2025). @@Korsunava V. I., Ponarin E. D. et al. (2024) Values in Crisis- International 2020–2022. Certificate No. RU 2024621301. URL: http://www1.fips.ru/fips_servi/fips_servlet? DB=DB&DocNumber=2024621301&TypeFile=html? (accessed 25.01.2025). (In Russ.)
  6. 6. Корсунова В. И., Соколов Б. О. Динамика поддержки эмансипативных ценностей в России в ходе пандемии COVID-19 // Социологический журнал. 2023. № 2. С. 8–24. DOI: 10.19181/socjour.2023.29.2.1. @@Korsunava V. I., Sokolov B. O. (2023) Support for Emancipative Values in Russia during the COVID-19 Pandemic. Sotsiologhetskiy zhurnal [Russian Sociological Journal]. Vol. 29. No. 2: 8–24. DOI: 10.19181/socjour.2023.29.2.1. (In Russ.)
  7. 7. Корсунова В. И., Соколов Б. О. Ценностные установки россыпи: сравнение результатов онлайн-и офлайн-опросов // Мониторинг общественного мнения: экономические и социальные перемены. 2022. № 3. С. 4–27. DOI: 10.14515/monitoring.2022.3.2083. @@Korsunava V. I., Sokolov B. O. (2022) Value Orientations in Russia: Comparing Evidence from Online and Face-to-Face Surveys. Monitoring obshthestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny [Monitoring of Public Opinion: Economic and Social Changes]. No. 3: 4–27. DOI: 10.14515/monitoring.2022.3.2083. (In Russ.)
  8. 8. Мавлетова А. М. Социологические опросы в сети Интернет: возможности построения типологии // Социология: 4 М. 2010. № 31. С. 115–134. @@Mavletova A. M. (2010). Sociological Online Surveys: How to Construct a Typology. Sociologia: 4M [Sociology: Methodology, Methods, Mathematical Modeling (Sociology: 4M)]. No. 31: 115–134. (In Russ.)
  9. 9. Некрасов С. И. Сравнение результатов онлайн-и оффлайн-опросов (на примере анкет разной сложности) // Социология: 4 М. 2011. № 32. С. 53–74. @@Nekrasov S. I. (2011) An Experimental Comparison of Online and Offline Survey Data: A Case of Questionnaires with Different Level of Difficulty. Sociologia: 4M [Sociology: Methodology, Methods, Mathematical Modeling (Sociology: 4M)]. No. 32: 53–74. (In Russ).
  10. 10. Соколов Б. О., Завадская М. А. Социально-демографические особенности, личностные черты, ценности и установки ковид-скептиков в России // Мониторинг общественного мнения: экономические и социальные перемены. 2021. № 6. С. 410–435. DOI: 10.14515/monitoring.2021.6.1938. @@Sokolov B. O., Zavadskaya M. A. (2021) Socio-Demographic Profiles, Personality Traits, Values, and Attitudes of COVID-Skeptics in Russia. Monitoring obshehestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny [Monitoring of Public Opinion: Economic and Social Changes]. No. 6: 410–435. DOI: 10.14515/monitoring.2021.6.1938. (In Russ).
  11. 11. Терентьев Е. А., Мавлетова А. М., Косолапов М. С. Интервьюирование с помощью компьютерных технологий в лонгитюдных обследованиях домохозяйств // Мониторинг общественного мнения: экономические и социальные перемены. 2018. № 3. С. 47–64. DOI: 10.14515/monitoring.2018.3.03. @@Terentev E. A., Navletova A. M., Kosolapov M. S. (2018) Computer-Assisted Personal Interviewing for Longitudinal Household Studies. Monitoring obshehestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny [Monitoring of Public Opinion: Economic and Social Changes]. No. 3: 47–64. DOI: 10.14515/monitoring.2018.3.03. (In Russ.)
  12. 12. Чуриков А. В. Основы построения выборки для социологических исследований. М.: Ин-т ф-да «Общественное мнение», 2020. @@Churikov A. V. (2020) Fundamentals of Sampling Design for Sociological Research. Moscow: In-t f-da "Obshthestvennoye mneniya" (In Russ.)
  13. 13. Arel-Bundock V, Greifer N, Heiss A. How to Interpret Statistical Models Using marginaleffects for R and Python // Journal of Statistical Software. 2024. No. 111(9). P. 1–32. DOI: 10.18637/jss.v111.i09.
  14. 14. Barber J., Kusunoki Y. et al. Participation in an Intensive Longitudinal Study with Weekly Web Surveys Over 2.5 Years // Journal of Medical Internet Research. 2016. Vol. 18. No. 6. P. e105. DOI: 10.2196/jmir.5422.
  15. 15. Bu F., Cernat A. et al. Online Survey Retention and Re-engagement: Learning from the COVID-19 Social Study // Field Methods. 2025. Vol. 37. No. 3. P. 244–259. DOI: 10.1177/152582X241289870.
  16. 16. Castorena O., Lupu N. et al. Online Surveys in Latin America // PS: Political Science & Politics. 2023. Vol. 56. No. 2. P. 273–280. DOI: 10.1017/S1049096522001287.
  17. 17. Deng Y., Hillygus D. S. et al. Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples // Statistical Science. 2013. Vol. 28. No. 2. P. 238–256. DOI: 10.1214/13-ST5414.
  18. 18. Frankel L. L., Hillygus D. S. Looking Beyond Demographics: Panel Attrition in the ANES and GSS // Political Analysis. 2014. Vol. 22. No. 3. P. 336–353. DOI: 10.1093/pan/mpt020.
  19. 19. Herron M. C. Postestimation Uncertainty in Limited Dependent Variable Models // Political Analysis. 1999. Vol. 8. No. 1. P. 83–98. DOI: 10.1093/oxfordjournals.pan.a029806.
  20. 20. Lüdecke D., Ben-Shachar M. S. et al. Performance: An R Package for Assessment, Comparison and Testing of Statistical Models // Journal of Open Source Software. 2021. Vol. 6. No. 60. P. 3139. DOI: 10.21105/joss.03139.
  21. 21. Lugtig P. Panel Attrition: Separating Stayers, Fast Attriters, Gradual Attriters, and Lurkers // Sociological Methods & Research. 2014. Vol. 43. No. 4. P. 699–723. DOI: 10.1177/0049124113520305.
  22. 22. Lynn P. Tackling Panel Attrition // The Palgrave Handbook of Survey Research / Ed. by D. L. Vannette, J. A. Krosnick. Cham, Switzerland: Palgrave Macmillan, 2017. P. 143–153.
  23. 23. Maslovskaya O., Lugtig P. Representativeness in Six Waves of Cross-National Online Survey (CRONOS) Panel // Journal of the Royal Statistical Society Series A: Statistics in Society. 2022. Vol. 185. No. 3. P. 851–871. DOI: 10.1111/rssa.12801.
  24. 24. R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing, 2024. URL: https://www.R-project.org/ (дата обращения: 25.01.2025).
  25. 25. Rübsamen N., Akmatov M. K. et al. Factors Associated with Attrition in a Longitudinal Online Study: Results from the HaBIDS Panel // BMC Medical Research Methodology. 2017. Vol. 17. P. 132. DOI: 10.1186/s12874-017-0408-3.
  26. 26. Yu T., Chen J. et al. Predicting Panel Attrition in Longitudinal HRQoL Surveys During the COVID-19 Pandemic in the US // Health and Quality of Life Outcomes. 2022. Vol. 20. P. 104. DOI: 10.1186/s12955-022-02015-8.
  27. 27. Zhang C., Antoun C. et al. Professional Respondents in Opt-In Online Panels: What Do We Really Know? // Social Science Computer Review. 2020. Vol. 38. No. 6. P. 703–719. DOI: 10.1177/0894439319845102.
  28. 28. Zhou H., Fishbach A. The Pitfall of Experimenting on the Web: How Unattended Selective Attrition Leads to Surprising (Yet False) Research Conclusions // Journal of Personality and Social Psychology. 2016. Vol. 111. No. 4. P. 493–504. DOI: 10.1037/pspa0000056.
QR
Translate

Индексирование

Scopus

Scopus

Scopus

Crossref

Scopus

Higher Attestation Commission

At the Ministry of Education and Science of the Russian Federation

Scopus

Scientific Electronic Library