Estimación con datos obtenidos con diseños muestrales mixtos que combinan muestreos de teléfonos móviles y de redes sociales.
M. M. Rueda García, L. Castro, B. Cobo Rodríguez, R. Ferri García, S. Pasadas del Amo
En los últimos años se han proporcionado diferentes métodos para combinar información de múltiples fuentes de datos. Nosotros nos centramos en caso de muestras probabilísticas y no probabilísticas que comparten el mismo cuestionario, combinando ambas para maximizar la eficiencia de las estimaciones con la ayuda de métodos de aprendizaje automático.
Desarrollamos un nuevo método de estimación para integrar datos de muestras probabilísticas y no probabilísticas, evaluamos la eficiencia de las estimaciones resultantes comparándolas con otras estrategias que se han utilizado antes. La aplicación de este método a la segunda ola de la Encuesta sobre el impacto de la pandemia de COVID-19 en España nos permite concluir que el método de estimación que proponemos es la mejor opción para reducir los sesgos observados en nuestros datos.
Keywords: encuestas no probabilísticas, técnicas de aprendizaje automático, Propensity score matching, muestreo de encuestas
Scheduled
Invited Session Data Analysis and Social Science. Data integration probabilistic, non-probabilistic surveys and big data
June 7, 2022 3:30 PM
A11
Other papers in the same session
B. Cobo Rodríguez, M. M. Rueda García, L. Calle López, F. López Torrecillas
L. Castro Martín, R. Ferri García, M. M. Rueda García
R. Ferri García, J. Beaumont, K. Bosa, J. Charlebois, K. Chu
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6/8/22
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