Using Facebook Advertising to Recruit Representative Samples: Feasibility Assessment of a Cross-Sectional Survey
Notice bibliographique
Résumé
BACKGROUND: Facebook has shown promise as an economical means of recruiting participants for health research. However, few studies have evaluated this recruitment method in Canada, fewer still targeting older adults, and, to our knowledge, none specifically in Newfoundland and Labrador (NL). OBJECTIVE: This study aimed to assess Facebook advertising as an economical means of recruiting a representative sample of adults aged 35 to 74 years in NL for a cross-sectional health survey. METHODS: Facebook advertising was used to recruit for a Web-based survey on cancer awareness and prevention during April and May 2018; during recruitment, additional advertisements were targeted to increase representation of demographics that we identified as being underrepresented in our sample. Sociodemographic and health characteristics of the study sample were compared with distributions of the underlying population to determine representativeness. Cramer V indicates the magnitude of the difference between the sample and population distributions, interpreted as small (Cramer V=0.10), medium (0.30), and large (0.50). Sample characteristics were considered representative if there was no statistically significant difference in distributions (chi-square P>.01) or if the difference was small (V≤0.10), and practically representative if 0.10<V≤0.20. The cost per recruit of Facebook advertising was compared with a quote for a random digit dialing (RDD)-recruited postal survey to determine if this method was economical. RESULTS: Facebook advertising is feasible and economical to conduct survey research, reaching 34,012 people, of which 2067 clicked on the ad, for a final sample size of 1048 people at Can $2.18 per recruit versus the quoted Can $23,316.05 for 400 recruits (Can $35.52 per recruit) via RDD. The sample was representative of rural and urban geography (P=.02; V=0.073), practically representative of age (P=.003; V=0.145) and income (P<.001; V=0.188), and over-representative of women (P<.001; V=0.507) and higher levels of education (P<.001; V=0.488). The sample was representative of the proportion of people with a regular health care provider (P=.94; V=0.025), diabetes prevalence (P=.002; V=0.096), and having had a colonoscopy or sigmoidoscopy (P=.27; V=0.034), and it was practically representative of smoking status (P<.001; V=0.14), and body mass index (P<.001; V=0.135). The sample was not representative of arthritis prevalence (P<.001; V=0.573), perceived health (P<.001; V=0.384), or time since last seasonal flu shot (P<.001; V=0.449). CONCLUSIONS: Facebook advertising offers an easy, rapid, and economical means to recruit a partially representative (representative or practically representative of 8 of the 13 characteristics studied) sample of middle-aged and older adults for health survey research. As Facebook uses a nonrandom targeting algorithm, caution is warranted in its applications for certain types of research.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,050 | 0,074 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».