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Analysis of categorical data for complex surveys

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  • Skinner, Chris J.

Abstract

This paper reviews methods for handling complex sampling schemes when analysing categorical survey data. It is generally assumed that the complex sampling scheme does not affect the specification of the parameters of interest, only the methodology for making inference about these parameters. The organisation of the paper is loosely chronological. Contingency table data is emphasized first before moving on to the analysis of unit-level data. Weighted least squares methods, introduced in the mid 1970s along with methods for two-way tables, receive early attention. They are followed by more general methods based on maximum likelihood, particularly pseudo maximum likelihood estimation. Point estimation methods typically involve the use of survey weights in some way. Variance estimation methods are described in broad terms. There is a particular emphasis on methods of testing. The main modelling methods considered are log-linear models, logit models, generalized linear models and latent variable models. There is no coverage of multilevel models.

Suggested Citation

  • Skinner, Chris J., 2018. "Analysis of categorical data for complex surveys," LSE Research Online Documents on Economics 89707, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:89707
    as

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    File URL: http://eprints.lse.ac.uk/89707/
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    References listed on IDEAS

    as
    1. Alastair Scott & Chris Wild, 2002. "On the robustness of weighted methods for fitting models to case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 207-219, May.
    2. Sundar Natarajan & Stuart R. Lipsitz & Garrett M. Fitzmaurice & Debajyoti Sinha & Joseph G. Ibrahim & Jennifer Haas & Walid Gellad, 2012. "An extension of the Wilcoxon rank sum test for complex sample survey data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 653-664, August.
    3. Clifford C. Clogg & Scott R. Eliason, 1987. "Some Common Problems in Log-Linear Analysis," Sociological Methods & Research, , vol. 16(1), pages 8-44, August.
    4. Chris Skinner & Louis-André Vallet, 2010. "Fitting Log-Linear Models to Contingency Tables From Surveys With Complex Sampling Designs: An Investigation of the Clogg-Eliason Approach," Sociological Methods & Research, , vol. 39(1), pages 83-108, August.
    5. Patterson B.H. & Dayton C.M. & Graubard B.I., 2002. "Latent Class Analysis of Complex Sample Survey Data: Application to Dietary Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 721-741, September.
    6. C., E. A. Molina & Skinner, C. J., 1992. "Pseudo-likelihood and quasi-likelihood estimation for complex sampling schemes," Computational Statistics & Data Analysis, Elsevier, vol. 13(4), pages 395-405, May.
    7. Yan Li & Barry I. Graubard & Ralph DiGaetano, 2011. "Weighting methods for population‐based case–control studies with complex sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(2), pages 165-185, March.
    8. Skinner, Chris J. & Vallet, L.-A., 2010. "Fitting log-linear models to contingency tables from surveys with complex sampling designs: an investigation of the Clogg-Eliason approach," LSE Research Online Documents on Economics 39118, London School of Economics and Political Science, LSE Library.
    9. Oberski, Daniel, 2014. "lavaan.survey: An R Package for Complex Survey Analysis of Structural Equation Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i01).
    10. Anders Christoffersson, 1975. "Factor analysis of dichotomized variables," Psychometrika, Springer;The Psychometric Society, vol. 40(1), pages 5-32, March.
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    More about this item

    Keywords

    pseudo maximum likelihood; Rao-Scott adjustment; score test; survey weight; weighted least squares;
    All these keywords.

    JEL classification:

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

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