Missing data techniques for structural equation modeling.
J Abnorm Psychol
; 112(4): 545-57, 2003 Nov.
Article
de En
| MEDLINE
| ID: mdl-14674868
ABSTRACT
As with other statistical methods, missing data often create major problems for the estimation of structural equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, structural equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods.
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Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Psychométrie
/
Psychopathologie
/
Modèles statistiques
Type d'étude:
Health_economic_evaluation
/
Risk_factors_studies
Limites:
Adult
/
Child
/
Humans
Langue:
En
Journal:
J Abnorm Psychol
Année:
2003
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique