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

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

A model is proposed for investigating test validity as a predictor of a criterion variable when there are both missing and censored scores in the data set. Implications for maximum likelihood estimation are discussed, and the method is illustrated with hypothetical data sets. (SLD)

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Bibliographische Detailangaben
Veröffentlicht in:Psychometrika 1990-09, Vol.55 (3), p.533-549
1. Verfasser: Gross, Alan L
Format: Artikel
Sprache:eng
Schlagworte:
Biological and medical sciences
Censored Data
EM Algorithm
Equations (Mathematics)
Fundamental and applied biological sciences. Psychology
Mathematical Models
Maximum Likelihood Statistics
Missing Data
Predictive Measurement
Predictor Variables
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Psychometrics
Psychometrics. Statistics. Methodology
Range Restriction
Scores
Test Validity
Testing Problems
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Beschreibung
Zusammenfassung:A model is proposed for investigating test validity as a predictor of a criterion variable when there are both missing and censored scores in the data set. Implications for maximum likelihood estimation are discussed, and the method is illustrated with hypothetical data sets. (SLD)
ISSN:0033-3123
1860-0980
DOI:10.1007/BF02294766