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The Treatment of Missing Data in Multivariate Analysis

Author

Listed:
  • Jae-On Kim

    (University of Iowa)

  • James Curry

    (University of Iowa)

Abstract

Procedures for treating missing data in the statistical analysis of survey data are reviewed. The main topics covered are: (1) how to assess the nature of missing data especially with regard to randomness, (2) a comparison of listwise and pairwise deletion, and (3) methods for using maximum information to estimate (a) parameters or (b) missing values.

Suggested Citation

  • Jae-On Kim & James Curry, 1977. "The Treatment of Missing Data in Multivariate Analysis," Sociological Methods & Research, , vol. 6(2), pages 215-240, November.
    • Handle: RePEc:sae:somere:v:6:y:1977:i:2:p:215-240
    DOI: 10.1177/004912417700600206
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    References listed on IDEAS

    as
    1. Henry Kaiser & Kern Dickman, 1962. "Sample and population score matrices and sample correlation matrices from an arbitrary population correlation matrix," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 179-182, June.
    2. Neil Timm, 1970. "The estimation of variance-covariance and correlation matrices from incomplete data," Psychometrika, Springer;The Psychometric Society, vol. 35(4), pages 417-437, December.
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    Cited by:

    1. Wan-Lun Wang & Min Liu & Tsung-I Lin, 2017. "Robust skew-t factor analysis models for handling missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 649-672, November.

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