Cargando…
Structural Equation Modeling With Many Variables: A Systematic Review of Issues and Developments
Survey data in social, behavioral, and health sciences often contain many variables (p). Structural equation modeling (SEM) is commonly used to analyze such data. With a sufficient number of participants (N), SEM enables researchers to easily set up and reliably test hypothetical relationships among...
Autores principales: | Deng, Lifang, Yang, Miao, Marcoulides, Katerina M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5932371/ https://www.ncbi.nlm.nih.gov/pubmed/29755388 http://dx.doi.org/10.3389/fpsyg.2018.00580 |
Ejemplares similares
-
Residual-Based Algorithm for Growth Mixture Modeling: A Monte Carlo Simulation Study
por: Marcoulides, Katerina M., et al.
Publicado: (2021) -
A Comparative Study on the Performance of GSCA and CSA in Parameter Recovery for Structural Equation Models With Ordinal Observed Variables
por: Jung, Kwanghee, et al.
Publicado: (2018) -
Model fit evaluation in multilevel structural equation models
por: Ryu, Ehri
Publicado: (2014) -
Bifactor exploratory structural equation modeling: A meta-analytic review of model fit
por: Gegenfurtner, Andreas
Publicado: (2022) -
Score-Guided Structural Equation Model Trees
por: Arnold, Manuel, et al.
Publicado: (2021)