scholarly journals Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling

2013 ◽  
Vol 35 (5) ◽  
pp. 2055-2072 ◽  
Author(s):  
Prasanna Karunanayaka ◽  
Paul J. Eslinger ◽  
Jian-Li Wang ◽  
Christopher W. Weitekamp ◽  
Sarah Molitoris ◽  
...  
Keyword(s):  
Structural Equation Modeling ◽  
Independent Component Analysis ◽  
Structural Equation ◽  
Component Analysis ◽  
Independent Component ◽  
Equation Modeling

scholarly journals Cutoff criteria for overall model fit indexes in generalized structured component analysis

2020 ◽  
Vol 8 (4) ◽  
pp. 189-202
Author(s):  
Gyeongcheol Cho ◽  
Heungsun Hwang ◽  
Marko Sarstedt ◽  
Christian M. Ringle
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Component Analysis ◽  
Model Fit ◽  
Equation Modeling ◽  
Generalized Structured Component Analysis ◽  
Root Mean Square Residual ◽  
Fit Indexes ◽  
Cutoff Criteria

AbstractGeneralized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.

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

scholarly journals ANALISIS DERAJAT KESEHATAN MASYARAKAT PROVINSI BALI DENGAN MENGGUNAKAN METODE GENERALIZED STRUCTURED COMPONENT ANALYSIS (GSCA)

2013 ◽  
Vol 2 (2) ◽  
pp. 54
Author(s):  
PUTU NOPITA PURNAMA NINGSIH ◽  
KETUT JAYANEGARA ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Structural Equation Modeling ◽  
Health Status ◽  
Health Service ◽  
Structural Equation ◽  
Component Analysis ◽  
Equation Modeling ◽  
Multivariate Normal ◽  
Behavioral Economic ◽  
Generalized Structured Component Analysis ◽  
The Relationship

The aim of this research is to determine the relationship between environmental, behavioral, health services, education, and economic variables to health status in the Province of Bali. These variables are constructs (latents ) that can not be measured directly by observation. If there was a relationship between latent and its indicators, it is recomended to use Structural Equation Modeling (SEM). In this research we used variance-based SEM i.e. Generalized Structured Component Analysis (GSCA). This method not based on many assumptions such as the data does not have a multivariate normal distribution, the sample size does is not necessary large. Moreover, GSCA provides by overall goodness-fit of the model. The result of this research indicates that the environmental, behavioral, economic and educational variable influenced health status, but health service does not significantly affect the health status; economic does not significantly affect the environment; and education does not significantly affect the behavior. The result of the FIT value ?0.450 and the AFIT value 0.429 showed that overall model in this research is not good enough because of both of these values are under 0.50.

scholarly journals STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA

2020 ◽  
Vol 13 (2) ◽  
pp. 136-148
Author(s):  
Epha Diana Supandi
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Work Motivation ◽  
Positive Impact ◽  
Employee Performance ◽  
Component Analysis ◽  
Equation Modeling ◽  
Multivariate Statistical ◽  
Generalized Structured Component Analysis ◽  
The Relationship

Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.

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