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How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses
COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-1...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9638233/ https://www.ncbi.nlm.nih.gov/pubmed/36335560 http://dx.doi.org/10.1007/s10654-022-00932-y |
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author | Joyal-Desmarais, Keven Stojanovic, Jovana Kennedy, Eric B. Enticott, Joanne C. Boucher, Vincent Gosselin Vo, Hung Košir, Urška Lavoie, Kim L. Bacon, Simon L. |
author_facet | Joyal-Desmarais, Keven Stojanovic, Jovana Kennedy, Eric B. Enticott, Joanne C. Boucher, Vincent Gosselin Vo, Hung Košir, Urška Lavoie, Kim L. Bacon, Simon L. |
author_sort | Joyal-Desmarais, Keven |
collection | PubMed |
description | COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00932-y. |
format | Online Article Text |
id | pubmed-9638233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-96382332022-11-07 How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses Joyal-Desmarais, Keven Stojanovic, Jovana Kennedy, Eric B. Enticott, Joanne C. Boucher, Vincent Gosselin Vo, Hung Košir, Urška Lavoie, Kim L. Bacon, Simon L. Eur J Epidemiol Covid-19 COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00932-y. Springer Netherlands 2022-11-06 2022 /pmc/articles/PMC9638233/ /pubmed/36335560 http://dx.doi.org/10.1007/s10654-022-00932-y Text en © Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Covid-19 Joyal-Desmarais, Keven Stojanovic, Jovana Kennedy, Eric B. Enticott, Joanne C. Boucher, Vincent Gosselin Vo, Hung Košir, Urška Lavoie, Kim L. Bacon, Simon L. How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses |
title | How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses |
title_full | How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses |
title_fullStr | How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses |
title_full_unstemmed | How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses |
title_short | How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses |
title_sort | how well do covariates perform when adjusting for sampling bias in online covid-19 research? insights from multiverse analyses |
topic | Covid-19 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9638233/ https://www.ncbi.nlm.nih.gov/pubmed/36335560 http://dx.doi.org/10.1007/s10654-022-00932-y |
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