Accès ouvert déclaré
2022
article
How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses
Keven Joyal‐Desmarais, Jovana Stojanovic, Eric B. Kennedy, Joanne Enticott, Vincent Gosselin Boucher, Hung Vo, Urška Košir, Kim Lavoie, Simon Bacon, Zahir Vally, Nora Grañana, Analía Verónica Losada, Jacqueline Boyle, Md Shajedur Rahman Shawon, Shrinkhala Dawadi, Helena Teede, Alexandra Kautzky‐Willer, Arobindu Dash, Marília Estêvam Cornélio, Marlus Karsten, Darlan Laurício Matte, Felipe Fossati Reichert, Ahmed M Abou-Setta, Shawn D. Aaron, Angela S. Alberga, Tracie A. Barnett, Silvana Barone, Ariane Bélanger‐Gravel, Sarah Bernard, Lisa Maureen Birch, Susan J. Bondy, Linda Booij, Roxane Borgès Da Silva, Jean Bourbeau, Rachel Burns, Tavis S. Campbell, Linda E. Carlson, Étienne Charbonneau, Kim Corace, Olivier Drouin, Francine M. Ducharme, Mohsen Farhadloo, Carl F. Falk, Richard Fleet, Michel Fournier, Gary Garber, Lise Gauvin, Jennifer Gordon, Roland Grad, Samir Gupta, Kim Hellemans, Catherine M. Herba, Heungsun Hwang, Jack Jedwab, Lisa Kakinami, Sunmee Kim, Joanne Liu, Colleen M. Norris, Sandra Peláez, Louise Pilote, Paul Poirier, Justin Presseau, Eli Puterman, Joshua A. Rash, Paula Aver Bretanha Ribeiro, Mohsen Sadatsafavi, Paramita Saha‐Chaudhuri, Eva Suarthana, SzeMan Tse, Michael Vallis, Nicolás Bronfman Caceres, Manuel S. Ortíz, Paula Repetto, Mariantonia Lemos, Angelos P. Kassianos, Naja Hulvej Rod, Mathieu Beraneck, Grégory Ninot, Beate Ditzen, Thomas Kubiak, Sam Codjoe, Lily Kpobi, Amos Laar, Theodora Skoura, Delfin Lovelina Francis, Naorem Kiranmala Devi, Sanjenbam Yaiphaba Meitei, Suzanne Tanya Nethan, Lancelot Pinto, Kallur Nava Saraswathy, Dheeraj Tumu, Silviana Lestari, Grace Wangge, Molly Byrne, Hannah Durand, Oonagh Meade, Chris Noone, Hagai Levine, Anat Zaidman‐Zait, Stefania Boccia, Ilda Hoxhaj, Stefania Paduano, Valeria Raparelli, Drieda Zaçe, Ala’S Aburub, Daniel Akunga, Richard Ayah, Chris Barasa, Pamela Godia, Elizabeth Kimani‐Murage, Nicholas Mutuku, Teresa Mwoma, Violet Naanyu, Jackim Nyamari, Hildah Oburu, Joyce Olenja, Dismas Ongore, Abdhalah Ziraba, Chiwoza Bandawe, LohSiew Yim, Ademola J. Ajuwon, Nisar Ahmed Shar, Bilal Ahmed Usmani, Rosario Mercedes Bartolini Martínez, Hilary Creed‐Kanashiro, Paula Simão, Pierre Claver Rutayisire, Abu Zeeshan Bari, Katarina Vojvodić, Iveta Nagyová, Jason Bantjes, Brendon Barnes, Bronwynè Coetzee, Ashraf Khagee, Tebogo Maria Mothiba, Rizwana Roomaney, Leslie Swartz, Juhee Cho, Man-gyeong Lee, Anne H. Berman, Nouha Saleh Stattin, Susanne Fischer, Debbie Hu, Yasi̇n Kara, Ceprail Şimşek, Bilge Üzmezoğlu, John Bosco Isunju, James Mugisha, Lucie Byrne‐Davis, Paula Griffiths, Jo Hart, William Johnson, Susan Michie, Nicola J. Paine, Emily Petherick, Lauren B. Sherar, Robert M. Bilder, Matthew M. Burg, Susan M. Czajkowski, Ken Freedland, Sherri Sheinfeld Gorin, Alison Holman, Jiyoung Lee, Gilberto López, Sylvie Naar, Michele L. Okun, Lynda H. Powell, Sarah D. Pressman, Tracey A. Revenson, John Ruiz, Sudha Sivaram, Johannes Thrul, Claudia Trudel‐Fitzgerald, Abehaw Yohannes, Rhea Navani, Kushnan Ranakombu, Daisuke Hayashi Neto, Tair Ben‐Porat, Anda I. Dragomir, Amandine Gagnon-Hébert, C. Gemme, Mahrukh Jamil, Lisa Maria Käfer, Ariany Marques Vieira, Tasfia Tasbih, Robbie Woods, Reyhaneh Yousefi, Tamila Roslyakova, Lilli Priesterroth, Shirly Edelstein, Ruth Snir, Yifat Uri, Mohsen Alyami, Comfort Sanuade, Olivia Crescenzi, Kyle Warkentin, Katya Grinko, Lalita Angne, Jigisha Jain, Nikita Mathur, Anagha Mithe, Sarah Nethan
21Citations signalées, ce qui n’est pas une note de qualité
11Institutions déclarées
4Pays d’affiliation déclarés
Rattachement africain : ca, au, il, us.
Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses
- Date Crossref
- 06/11/2022
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Les institutions déclarées
Une affiliation ne permet pas de déduire la nationalité d’un auteur.
Les sujets associés
Vaccine Coverage and HesitancyCOVID-19 epidemiological studiesMisinformation and Its Impacts