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Sampling behaviour in estimating predictive validity in the context of selection and latent variable modelling: A Monte Carlo study
Author(s) -
Hsu JinWen Yang
Publication year - 1995
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.2044-8317.1995.tb01051.x
Subject(s) - estimator , monte carlo method , statistics , context (archaeology) , latent variable , selection (genetic algorithm) , econometrics , sampling (signal processing) , predictive validity , sample (material) , mathematics , computer science , machine learning , paleontology , chemistry , filter (signal processing) , chromatography , computer vision , biology
While the effect of selection in predictive validity studies has long been recognized and discussed in psychometric studies, little consideration has been given to this problem in the context of latent variable models. In a recent paper, Muthén & Hsu (1993) proposed and compared estimators of predictive validity of a multifactorial test. Both selectivity and measurement error were considered in the estimation of predictive validity. The purpose of the present paper is to expand on Muthén & Hsu (1993) by examining and comparing the sampling behaviour of three estimators for predictive validity, LQL (listwise, quasi‐likelihood estimator), FQL (full, quasi‐likelihood estimator) and FS (factor score estimator), using a Monte Carlo approach. Effects of selection procedures, selection ratios and sample sizes on the sampling behaviours of the estimators are also investigated. The results show that FQL and FS are the two preferred estimators and each has different strengths and weaknesses. A real data application is presented to illustrate the practical implementation of the estimators.