Why generalized structured component analysis is not universally preferable to structural equation modeling

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.

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

E-Jurnal Matematika ◽

10.24843/mtk.2013.v02.i02.p039 ◽

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.

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An approach to structural equation modeling with both factors and components: Integrated generalized structured component analysis.

Psychological Methods ◽

10.1037/met0000336 ◽

2020 ◽

Cited By ~ 1

Author(s):

Heungsun Hwang ◽

Gyeongcheol Cho ◽

Kwanghee Jung ◽

Carl F. Falk ◽

Jessica Kay Flake ◽

...

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Component Analysis ◽

Equation Modeling ◽

Generalized Structured Component Analysis

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

MEDIA STATISTIKA ◽

10.14710/medstat.13.2.136-148 ◽

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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Supplemental Material for An Approach to Structural Equation Modeling With Both Factors and Components: Integrated Generalized Structured Component Analysis

Psychological Methods ◽

10.1037/met0000336.supp ◽

2020 ◽

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Component Analysis ◽

Equation Modeling ◽

Generalized Structured Component Analysis

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PENERAPAN METODE GENERALIZED STRUCTURED COMPONENT ANALYSIS PADA KEPUASAN KONSUMEN (Studi Kasus: Pasien Klinik Q)

Jurnal Gaussian ◽

10.14710/j.gauss.v9i4.29416 ◽

2020 ◽

Vol 9 (4) ◽

pp. 454-463

Author(s):

Farisiyah Fitriani ◽

Agus Rusgiyono ◽

Tatik Widiharih

Keyword(s):

Structural Equation Modeling ◽

Latent Variables ◽

Random Sampling ◽

Structural Equation ◽

Goodness Of Fit ◽

Component Analysis ◽

Least Square ◽

Equation Modeling ◽

Generalized Structured Component Analysis ◽

The Impact

Customer satisfaction is used by a company to evaluate products or services whether it is sufficient with customer’s expectations. Satisfaction is influenced by factors that cannot be measured directly are called latent variables and can be measured through indicators used to measure satisfaction with Structural Equation Modeling (SEM). Generalized Structured Component Analysis (GSCA) method is part of a SEM based on a variant that does not require the assumption of a multivariate normal distribution and has a measure overall goodness of fit. The parameters used are factor loading, coefficients parameter, and weight of indicators and estimated with alternating least square. The type of data used primary data from the results of the questionnaire with stratified proportional random sampling and number of samples 286. This research using indicators as measurable variables as many 32 indicators and 8 latent variable. Considering to the evaluation of the structural model, it is found there are 5 variables that influence satisfaction, they are prices, quality yield, cleanliness, doctor's services, and employee services with a large influence of 77.18% and the impact of satisfaction on loyalty is 48.63 %. For the overall goodness of fit measure, it is known that the FIT value is 63.75% and the adjusted FIT (AFIT) value is 63.47%. The goodness of fit (GFI) produced the value in the amount of 96.43%, indicating that the general model has the good level of compatibility.Keywords: Generalized Structured Component Analysis, Structural Equation Modeling, Overall goodness of fit, Alternating Least Square, Stratified Proportional Random Sampling

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Principal Component Analysis, Factor Analysis, and Structural Equation Modeling: A Very Brief Introduction

Statistical Test Theory for the Behavioral Sciences ◽

10.1201/9781584889595-11 ◽

2007 ◽

pp. 141-148

Keyword(s):

Principal Component Analysis ◽

Factor Analysis ◽

Structural Equation Modeling ◽

Structural Equation ◽

Principal Component ◽

Component Analysis ◽

Equation Modeling

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

Jurnal ILMU DASAR ◽

10.19184/jid.v16i1.534 ◽

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

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Efficiency of Cluster Validity Indexes in Fuzzy Clusterwise Generalized Structured Component Analysis

Symmetry ◽

10.3390/sym12091514 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1514

Author(s):

Ji Hoon Ryoo ◽

Seohee Park ◽

Seongeun Kim ◽

Hyun Suk Ryoo

Keyword(s):

Statistical Model ◽

Fuzzy Clustering ◽

Structural Equation ◽

Component Analysis ◽

Optimal Number ◽

Equation Modeling ◽

Cluster Validity ◽

Generalized Structured Component Analysis ◽

Validation Measure ◽

Fit Indexes

Fuzzy clustering has been broadly applied to classify data into K clusters by assigning membership probabilities of each data point close to K centroids. Such a function has been applied into characterizing the clusters associated with a statistical model such as structural equation modeling. The characteristics identified by the statistical model further define the clusters as heterogeneous groups selected from a population. Recently, such statistical model has been formulated as fuzzy clusterwise generalized structured component analysis (fuzzy clusterwise GSCA). The same as in fuzzy clustering, the clusters are enumerated to infer the population and its parameters within the fuzzy clusterwise GSCA. However, the identification of clusters in fuzzy clustering is a difficult task because of the data-dependence of classification indexes, which is known as a cluster validity problem. We examined the cluster validity problem within the fuzzy clusterwise GSCA framework and proposed a new criterion for selecting the most optimal number of clusters using both fit indexes of the GSCA and the fuzzy validity indexes in fuzzy clustering. The criterion, named the FIT-FHV method combining a fit index, FIT, from GSCA and a cluster validation measure, FHV, from fuzzy clustering, performed better than any other indices used in fuzzy clusterwise GSCA.

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