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On Modeling Missing Data in Structural Investigations Based on Tetrachoric Correlations With Free and Fixed Factor Loadings
In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlati...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
SAGE Publications
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638985/ https://www.ncbi.nlm.nih.gov/pubmed/37970487 http://dx.doi.org/10.1177/00131644221143145 |