Likelihood based procedures for general nonlinear structural equation analysis

2002 ◽  
Author(s):  
Yan Zhao
2001 ◽  
Vol 26 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Melanie M. Wall ◽  
Yasuo Amemiya

Interest in considering nonlinear structural equation models is well documented in the behavioral and social sciences as well as in the education and marketing literature. This article considers estimation of polynomial structural models. An existing method is shown to have a limitation that the produced estimator is inconsistent for most practical situations. A new procedure is introduced and defined for a general model using products of observed indicators. The resulting estimator is consistent without assuming any distributional form for the underlying factors or errors. Identification assessment and standard error estimation are discussed. A simulation study addresses statistical issues including comparisons of discrepancy functions and the choice of appended product indicators. Application of the new procedure in a substance abuse prevention study is also reported.


1996 ◽  
Vol 22 (1) ◽  
pp. 163-183 ◽  
Author(s):  
Robert A. Ping

Because there is little guidance for substantive researchers, the paper reviews the techniques for detecting latent variable interactions and quadratics. After examining plausible research situations where including these nonlinear variables might be appropriate, the paper describes the available detection techniques. Recent advances in nonlinear structural equation analysis are given particular attention, and suggestions for substantive researchers are made.


2021 ◽  
Vol 95 ◽  
pp. 103133
Author(s):  
Hany M. Hassan ◽  
Mark R. Ferguson ◽  
Brenda Vrkljan ◽  
Bruce Newbold ◽  
Saiedeh Razavi

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