L. Castro Martín, R. Ferri García, M. M. Rueda García

La comodidad de las encuestas online ha supuesto un importante aumento en su popularidad a la hora de recolectar datos. Sin embargo, normalmente no siguen una metodología probabilística. Las muestras no-probabilísticas implican importantes problemas de sesgo, cobertura y falta de respuesta que invalidan la fiabilidad de los resultados obtenidos. Algunos métodos han sido propuestos para corregir estos problemas, como Propensity Score Adjustment o Statistical Matching. En este trabajo hacemos un recorrido a través de las diversas técnicas del estado del arte del Machine Learning que pueden integrarse con dichos métodos para mejorar significativamente las estimaciones obtenidas.

Keywords: machine learning, muestreo, no-probabilístico


Invited Session Data Analysis and Social Science. Data integration probabilistic, non-probabilistic surveys and big data
June 7, 2022  3:30 PM

Other papers in the same session

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

Estimación del sesgo de deseabilidad social en trastornos de conducta en alimentación.

B. Cobo Rodríguez, M. M. Rueda García, L. Calle López, F. López Torrecillas

Weight smoothing for nonprobability surveys adjustments with multiple variables of interest

R. Ferri García, J. Beaumont, K. Bosa, J. Charlebois, K. Chu

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