Summary
In a practical analysis of data, the problem of incomplete data matrices is usually solved by estimating and substituting the missing values. Nevertheless, the imputation of missing values is only appropriate if the data are randomly missing. In general, the appropriate use of any missing-data method requires fundamental knowledge of the reasons and the underlying missing-data mechanism. With an analysis of the structure of the incomplete data matrix, the effects of the missing-data mechanism to the data under consideration can be investigated. Regarding the possible relations of dependence concerning the missing data, there are a few methods to test the existence of a non-systematic missing-data mechanism. The results of these tests can be sufficient conditions to reject the acceptance of randomly missing data or necessary conditions to accept a non-systematic missing-data mechanism.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
KIM, J.O., CURRY, J. (1977): The Treatment of Missing Data in Multivariate Analysis. Sociological Methods and Research, 6, 215–239.
LITTLE, R.J.A. (1988): A Test of Missing Completely at Random for Multivariate Data with Missing Values. Journal of the American Statistical Association, 83, 1198–1202.
LÖSEL, F., WÜSTENDÖRFER, W. (1974): Zum Problem unvollständiger Datenmatrizen in der empirischen Sozialforschung. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 26, 342–357.
RUBIN, D.B. (1976): Inference and Missing Data. Biometrika, 63, 581–592.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin · Heidelberg
About this paper
Cite this paper
Bankhofer, U. (1996). Incomplete Data Matrices and Tests on Randomly Missing Data. In: Gaul, W., Pfeifer, D. (eds) From Data to Knowledge. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79999-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-79999-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60354-2
Online ISBN: 978-3-642-79999-0
eBook Packages: Springer Book Archive