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Variance estimation in the analysis of clustered longitudinal survey data

Author

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  • Skinner, Chris J.
  • de Toledo Vieira, Marcel

Abstract

We investigate the impact of cluster sampling on standard errors in the analysis of longitudinal survey data. We consider a widely used class of regression models for longitudinal data and a standard class of point estimators of a generalized least squares type. We argue theoretically that the impact of ignoring clustering in standard error estimation will tend to increase with the number of waves in the analysis, under some patterns of clustering which are realistic for many social surveys. The implication is that it is, in general, at least as important to allow for clustering in standard errors for longitudinal analyses as for crosssectional analyses. We illustrate this theoretical argument with empirical evidence from a regression analysis of longitudinal data on gender role attitudes from the British Household Panel Survey. We also compare two approaches to variance estimation in the analysis of longitudinal survey data: a survey sampling approach based upon linearization and a multilevel modelling approach. We conclude that the impact of clustering can be seriously underestimated if it is simply handled by including an additive random effect to represent the clustering in a multilevel model.

Suggested Citation

  • Skinner, Chris J. & de Toledo Vieira, Marcel, 2007. "Variance estimation in the analysis of clustered longitudinal survey data," LSE Research Online Documents on Economics 39106, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:39106
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    File URL: http://eprints.lse.ac.uk/39106/
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    Citations

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    Cited by:

    1. Ana Isabel Polo Peña & Dolores María Frías Jamilena & Miguel Ángel Rodríguez Molina, 2017. "The effects of perceived value on loyalty: the moderating effect of market orientation adoption," Service Business, Springer;Pan-Pacific Business Association, vol. 11(1), pages 93-116, March.
    2. Eziyi Ibem & Dolapo Amole, 2014. "Satisfaction with Life in Public Housing in Ogun State, Nigeria: A Research Note," Journal of Happiness Studies, Springer, vol. 15(3), pages 495-501, June.
    3. Alinne Veiga & Peter W. F. Smith & James J. Brown, 2014. "The use of sample weights in multivariate multilevel models with an application to income data collected by using a rotating panel survey," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 65-84, January.
    4. Brajendra C. Sutradhar, 2022. "Multinomial Logistic Mixed Models for Clustered Categorical Data in a Complex Survey Sampling Setup," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 743-789, August.
    5. Oǧuz-Alper, Melike & Berger, Yves G., 2020. "Modelling multilevel data under complex sampling designs: An empirical likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    6. Brajendra C. Sutradhar, 2021. "Two Stage Cluster Sampling Based Asymptotic Inferences in Survey Population Models for Longitudinal Count and Categorical Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 26-69, February.

    More about this item

    Keywords

    clustering; design effect; misspecification effect; multilevel model;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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