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A Maximum Likelihood Approach to Test Validation with Missing and Censored Dependent Variables

Published online by Cambridge University Press:  01 January 2025

Alan L. Gross
Alan L. Gross*
Affiliation:
Department of Educational Psychology, Graduate Center, City University of New York
*
Requests for reprints should be sent to Alan L. Gross, Graduate Center, City University of New York, 33 West 42 Street, New York, NY 10036.
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Abstract

A maximum likelihood approach is described for estimating the validity of a test (x) as a predictor of a criterion variable (y) when there are both missing and censored y scores present in the data set. The missing data are due to selection on a latent variable (ys) which may be conditionally related to y given x. Thus, the missing data may not be missing random. The censoring process in due to the presence of a floor or ceiling effect. The maximum likelihood estimates are constructed using the EM algorithm. The entire analysis is demonstrated in terms of hypothetical data sets.

Keywords

missing datacensored dataEM algorithmrestriction of range
Type
Original Paper
Information
Psychometrika , Volume 55 , Issue 3 , September 1990 , pp. 533 - 549
Copyright
Copyright © 1990 The Psychometric Society

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