Can ridge and Elastic net structural equation modeling be used to stabilize parameter estimates when latent factors are correlated?

2021 ◽  
pp. 1-13
Author(s):  
Florian Scharf ◽  
Jana Pförtner ◽  
Steffen Nestler
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Elastic Net ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Latent Factors

Challenges in Nonlinear Structural Equation Modeling

Methodology ◽  
2007 ◽  
Vol 3 (3) ◽  
pp. 100-114 ◽  
Author(s):  
Polina Dimitruk ◽  
Karin Schermelleh-Engel ◽  
Augustin Kelava ◽  
Helfried Moosbrugger
Keyword(s):  
Structural Equation Modeling ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Nonlinear Effects ◽  
Structural Equations ◽  
Estimation Methods ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Nonlinear Structural ◽  
Nonlinear Structural Equation

Abstract. Challenges in evaluating nonlinear effects in multiple regression analyses include reliability, validity, multicollinearity, and dichotomization of continuous variables. While reliability and validity issues are solved by employing nonlinear structural equation modeling, multicollinearity remains a problem which may even be aggravated when using latent variable approaches. Further challenges of nonlinear latent analyses comprise the distribution of latent product terms, a problem especially relevant for approaches using maximum likelihood estimation methods based on multivariate normally distributed variables, and unbiased estimates of nonlinear effects under multicollinearity. The only methods that explicitly take the nonnormality of nonlinear latent models into account are latent moderated structural equations (LMS) and quasi-maximum likelihood (QML). In a small simulation study both methods yielded unbiased parameter estimates and correct estimates of standard errors for inferential statistics. The advantages and limitations of nonlinear structural equation modeling are discussed.

9. Structural Equation Modeling with Robust Covariances

1998 ◽  
Vol 28 (1) ◽  
pp. 363-396 ◽  
Author(s):  
Ke-Hai Yuan ◽  
Peter M. Bentler
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Structural Model ◽  
Likelihood Function ◽  
Breakdown Point ◽  
Individual Case ◽  
Equation Modeling ◽  
Parameter Estimates ◽  
Sample Covariance ◽  
Bounded Influence

Existing methods for structural equation modeling involve fitting the ordinary sample covariance matrix by a proposed structural model. Since a sample covariance is easily influenced by a few outlying cases, the standard practice of modeling sample covariances can lead to inefficient estimates as well as inflated fit indices. By giving a proper weight to each individual case, a robust covariance will have a bounded influence function as well as a nonzero breakdown point. These robust properties of the covariance estimators will be carried over to the parameter estimators in the structural model if a technically appropriate procedure is used. We study such a procedure in which robust covariances replace ordinary sample covariances in the context of the Wishart likelihood function. This procedure is easy to implement in practice. Statistical properties of this procedure are investigated. A fit index is given based on sampling from an elliptical distribution. An estimating equation approach is used to develop a variety of robust covariances, and consistent covariances of these robust estimators, needed for standard errors and test statistics, follow from this approach. Examples illustrate the inflated statistics and distorted parameter estimates obtained by using sample covariances when compared with those obtained by using robust covariances. The merits of each method and its relevance to specific types of data are discussed.

scholarly journals Structural Equation Modeling of the Factors Affecting the Nutritional Status of Children Under Five in Banyuwangi Region using Recursive (one-way) GSCA

2015 ◽  
Vol 16 (1) ◽  
pp. 1
Author(s):  
I Made Tirta ◽  
Nawal Ika Susanti ◽  
Yuliani Setia Dewi
Keyword(s):  
Structural Equation Modeling ◽  
Nutritional Status ◽  
Structural Equation ◽  
Economic Status ◽  
Bootstrap Method ◽  
Component Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Parameter Estimates

Structural Equation Modeling is one among popular multivariate analysis, especially applied in pschology and marketing. There are two main types of Structural Equation Modeling namely covariance-based or CB-SEM and variance-based or Partial Least Square (PLS)- SEM. Both types have advantages and disadvantage. To overcome its limitation, Generalized Structured Component Analysis (GSCA) was then proposed as an extension of PLS-SEM. In estimating the parameters, GSCA uses Alternating Least Squares (ALS) and in estimating the standard error of the parameter estimates it uses the bootstrap method. In this paper, GSCA is applied to study the causality model of Infant nutritional status, in relation with socio-economic status and infantcare status in Banyuwangi Region. The results show that both socio-economic and infantcare status have significant positive influence on infant nutritional status.Keywords:  Alternating least square, generalized structural component analysis,  nutritional status of infants,  structural equation modelling

Fungible parameter estimates in structural equation modeling.

2018 ◽  
Vol 23 (1) ◽  
pp. 58-75 ◽  
Author(s):  
Taehun Lee ◽  
Robert C. MacCallum ◽  
Michael W. Browne
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Equation Modeling ◽  
Parameter Estimates

scholarly journals Associating puberty, metabolism and cognition

2021 ◽  
Author(s):  
Rafaella Sales de Freitas ◽  
Fausto Coutinho-Lourenco ◽  
Hugo Cogo-Moreira ◽  
Allan Chiaratti Oliveira ◽  
Sabine Pompeia
Keyword(s):  
Socioeconomic Status ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Block Design ◽  
Metabolic Changes ◽  
Metabolic Status ◽  
Equation Modeling ◽  
Latent Factors ◽  
Intelligence Scale ◽  
Pubertal Status

Cognition is influenced by pubertal and metabolic changes independently. However, these changes co-occur in adolescence and their combined role on cognition is unclear. We used Structural Equation Modeling (SEM) to investigated the association of pubertal and metabolic status latent factors, each composed of a pool of indicators, on intelligence markers in 278 early adolescents, cross-sectionally. The SEM model, controlled for socioeconomic status, included paths between latent factors and from them to the outcomes: two subtests of Wechsler Abbreviated Intelligence Scale (Block Design; Vocabulary). Metabolic status related to pubertal status only in girls, but did not affect performance. Differently, more advanced pubertal status (controled for age) was positively associated with better verbal (Vocabulary) and non-verbal (Block Design) intelligence in both sexes.

scholarly journals Destination Image of Himachal Pradesh: Foreign Tourist Perception

2021 ◽  
Vol 19 (2) ◽  
pp. 112-124
Author(s):  
Sunil Kumar ◽  
◽  
Sandeep Sharma ◽  
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Major Element ◽  
Himachal Pradesh ◽  
Destination Image ◽  
Equation Modeling ◽  
Latent Factors ◽  
Affective Dimension ◽  
Foreign Nationals ◽  
Present Experience

Destination Image is a psychological set of emotions in a tourist’s mind. This study is an inquiry from 384 Foreign Nationals who included destination Himachal in their visit to India. The latent factors of ‘destination image’ were extracted and validated through structural equation modeling (SEM). Out of the ten explored latent factors ‘affective’ dimension is the major element followed by ‘infrastructure’, ‘culture’ and ‘safety’ are acting as significant persuading forces in destination image formation. The study will help various organizations and agencies to position their tourism products. Future researchers can investigate the change in destination image with other locations having the same geographical conditions. This study is limited to the present experience of tourists and didn’t include the revisit experiences of foreign tourists.

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