A proposal for handling missing data


Journal Article

A method for dealing with the problem of missing observations in multivariate data is developed and evaluated. The method uses a transformation of the principal components of the data to estimate missing entries. The properties of this method and four alternative methods are investigated by means of a Monte Carlo study of 42 computer-generated data matrices. The methods are compared with respect to their ability to predict correlation matrices as well as missing entries. The results indicate that whenever there exists modest intercorrelations among the variables (i.e., average off diagonal correlation above .2) the proposed method is at least as good as the best alternative (a regression method) while being considerably faster and simpler computationally. Models for determining the best alternative based upon easily calculated characteristics of the matrix are given. The generality of these models is demonstrated using the previously published results of Timm. © 1975 Psychometric Society.

Full Text

Duke Authors

Cited Authors

  • Gleason, TC; Staelin, R

Published Date

  • June 1, 1975

Published In

Volume / Issue

  • 40 / 2

Start / End Page

  • 229 - 252

Electronic International Standard Serial Number (EISSN)

  • 1860-0980

International Standard Serial Number (ISSN)

  • 0033-3123

Digital Object Identifier (DOI)

  • 10.1007/BF02291569

Citation Source

  • Scopus