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Redressements de la premiere vague de l’enquete EpiCov : un exemple de correction des effets de selection dans les enquetes multimodes

Author

Listed:
  • L. CASTELL

    (Insee)

  • C. FAVRE-MARTINOZ

    (Insee)

  • N. PALIOD

    (Insee)

  • P. SILLARD

    (Insee)

Abstract

La première vague de l’enquête Épidémiologie et Conditions de vie (EpiCov) a été collectée en mai 2020 dans le contexte de la pandémie de Covid-19 pour en mesurer l’impact sanitaire et social. Cette enquête est originale à plusieurs titres : thématique, objectif de diffusion départementale, mode de collecte essentiellement auto-administrée, réalisation d’auto-prélèvements. Ce document de travail décrit les redressements réalisés par l’Insee sur la première vague de l’enquête pour s’assurer de la qualité des résultats. Ces redressements prennent en compte les spécificités de l’enquête pour corriger le biais de non-réponse, concernant d’une part le questionnaire et d’autre part les auto-prélèvements. Cependant, ces méthodes de correction usuelles ne s’avèrent pas suffisantes pour corriger certaines variables d’intérêt de l’enquête comme les symptômes déclarés dans l’enquête qui ont affecté les répondants. En théorie, le biais observé sur ces variables peut s’expliquer par une erreur de mesure, liée à l’utilisation de plusieurs modes de collecte, ou par l’existence d’une sélection liée, de manière résiduelle, aux variables d’intérêt, non corrigée par les méthodes de correction de la non-réponse sur variables observables. On montre dans ce document que le biais procède d’une sélection liée aux variables d’intérêt et non d'une erreur de mesure associée au mode. Une méthode de correction de la sélection liée aux variables d’intérêt, basée sur un modèle de sélection d’Heckman, est mise en œuvre pour estimer sans biais les valeurs moyennes des variables d’intérêt concernées. Pour les variables de symptômes, la correction peut représenter plus de 50 % du niveau de prévalence déclaré non-corrigé. Cette correction conduit à réduire le niveau de prévalence des symptômes, conformément à l’idée selon laquelle les répondants à l’enquête semblent, en moyenne, davantage affectés par les symptômes de la maladie que les non-répondants.

Suggested Citation

  • L. Castell & C. Favre-Martinoz & N. Paliod & P. Sillard, 2023. "Redressements de la premiere vague de l’enquete EpiCov : un exemple de correction des effets de selection dans les enquetes multimodes," Documents de Travail de l'Insee - INSEE Working Papers m2023-02, Institut National de la Statistique et des Etudes Economiques.
    • Handle: RePEc:nse:doctra:m2023-02
    as

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    File URL: https://www.insee.fr/fr/statistiques/fichier/version-html/7452990/M2023-02.pdf
    File Function: Document de travail "Méthodologie Statistique" de la DMCSI numéro M2023/02
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    References listed on IDEAS

    as
    1. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    3. David Haziza & Jean‐François Beaumont, 2007. "On the Construction of Imputation Classes in Surveys," International Statistical Review, International Statistical Institute, vol. 75(1), pages 25-43, April.
    4. L. Castell & P. Sillard, 2021. "Le traitement du biais de selection endogene dans les enquetes aupres des menages par modele de Heckman," Documents de Travail de l'Insee - INSEE Working Papers m2021-02, Institut National de la Statistique et des Etudes Economiques.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    EpiCov; enquete; multimode; selection endogene; modele d’Heckman; non-reponse;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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