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Evaluation of latent construct correlations in the presence of missing data: A note on a latent variable modelling approach
Author(s) -
Raykov Tenko
Publication year - 2012
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 - 10.1348/000711010x498162
Subject(s) - missing data , latent variable , covariate , latent variable model , local independence , latent class model , mathematics , statistics , econometrics , construct (python library) , computer science , programming language
A latent variable modelling approach is discussed, which can be used to evaluate indices of linear relationship between latent constructs in incomplete data sets. The method is based on an application of maximum‐likelihood estimation and inclusion of covariates predictive of missing values. The approach can be employed for point and interval estimation of latent correlations in the presence of missing data, and capitalizes on enhanced plausibility of the assumption of data missing at random through introduction of informative covariates. The method is illustrated on empirical data.