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Selection and predictive validity with latent variable structures †
Author(s) -
Muthén Bengt O.,
Hsu JinWen Yang
Publication year - 1993
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.1993.tb01015.x
Subject(s) - estimator , missing data , statistics , population , latent variable , econometrics , selection (genetic algorithm) , regression , mathematics , sample (material) , regression analysis , computer science , artificial intelligence , demography , sociology , chemistry , chromatography
Estimators of the predictive validity of a multifactorial test are considered. These estimators take into account the selectivity of the sample of those who have observations on the criterion measure. It is pointed out that the selectivity problem can be viewed as a missing data situation. The relationships between the classic Pearson‐Lawley adjustment, regression based on factor scores, and maximum‐likelihood estimation under ignorable missingness are described. The estimators are compared in a study of artificial population data.