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An Evaluation of Non-Iterative Estimators in the Structural after Measurement (SAM) Approach to Structural Equation Modeling (SEM)
被引:3
作者:
Dhaene, Sara
[1
]
Rosseel, Yves
[1
]
机构:
[1] Univ Ghent, Ghent, Belgium
关键词:
Maximum likelihood;
non-iterative estimators;
structural after measurement;
structural equation modeling;
CONFIRMATORY FACTOR-ANALYSIS;
IMPROPER SOLUTIONS;
FIT INDEXES;
R PACKAGE;
LATENT;
VARIABLES;
ERROR;
D O I:
10.1080/10705511.2023.2220135
中图分类号:
O1 [数学];
学科分类号:
0701 ;
070101 ;
摘要:
In Structural Equation Modeling (SEM), the measurement part and the structural part are typically estimated simultaneously via an iterative Maximum Likelihood (ML) procedure. In this study, we compare performance of the standard procedure to the Structural After Measurement (SAM) approach, where the structural part is separated from the measurement part. One appealing feature of the latter multi-step procedure is that it extends the scope of possible estimators, as now also non-iterative methods from factor-analytic literature can be used to estimate the measurement models. In our simulations, the SAM approach outperformed vanilla SEM in small to moderate samples (i.e., no convergence issues, no inadmissible solutions, smaller MSE values). Notably, this held regardless of the estimator used for the measurement part, with negligible differences between iterative and non-iterative estimators. This may call into question the added value of advanced iterative algorithms over closed-form expressions (which generally require less computational time and resources).
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页码:926 / 940
页数:15
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