scholarly journals Maximum Likelihood Estimation and Inference for High Dimensional Nonlinear Factor Models with Application to Factor-Augmented Regressions

2018 ◽  
Author(s):  
Fa Wang
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Factor Models ◽  
High Dimensional ◽  
Nonlinear Factor

scholarly journals Quantifying the strength of general factors in psychopathology: A comparison of CFA with maximum likelihood estimation, BSEM and ESEM/EFA bi-factor approaches

2018 ◽  
Author(s):  
Aja Louise Murray ◽  
Tom Booth ◽  
Manuel Eisner ◽  
Ingrid Obsuth ◽  
Denis Ribeaud
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Factor Models ◽  
General Factor ◽  
Normative Sample ◽  
Ml Estimation ◽  
Co Morbidity ◽  
Simulation Results ◽  
P Factor

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p-factor) in classifying, researching, diagnosing and treating psychiatric disorders depends (amongst other issues) on the extent to which co-morbidity is symptom-general rather than staying largely within the confines of narrower trans-diagnostic factors such as internalising and externalising. In this study we compared three methods of estimating p-factor strength. We compared omega hierarchical and ECV calculated from CFA bi-factor models with maximum likelihood (ML) estimation, from ESEM/EFA models with a bifactor rotation, and from BSEM bi-factor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings may be the preferred option. However, CFA with ML also performed well provided secondary loadings were modelled We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n=1286) and University counselling sample (n= 359).

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