scholarly journals Generalized Structured Component Analysis to Analyze Measurement Models: Utilization of Health Insurance

2021 ◽  
Vol 1863 (1) ◽  
pp. 012042
Author(s):  
D V Ferezagia ◽  
K A Safitri ◽  
N F Dewi ◽  
D Anggara
Keyword(s):  
Health Insurance ◽  
Component Analysis ◽  
Generalized Structured Component Analysis ◽  
Measurement Models

scholarly journals MENTAL ACCOUNTING DAN DAMPAKNYA TERHADAP KINERJA PERUSAHAAN MELALUI PENEMPATAN MODAL KERJA PADA USAHA KECIL DAN MENENGAH DI PULAU LOMBOK

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Siti Aisyah Hidayati
Keyword(s):  
Data Analysis ◽  
Inventory Management ◽  
Component Analysis ◽  
Mental Accounting ◽  
Company Performance ◽  
Working Capital ◽  
Probability Sampling ◽  
Generalized Structured Component Analysis ◽  
Pottery Industry ◽  
Lombok Island

This study aims at analyzing: 1)  the effect of mental accounting on placement of funds  for working capital in small- and middle-scale business, 2) the effect of  placement of funds  for working capital on company performance in small-and middle-scale business, and 3) the effect of mental accounting on company performance through placement of funds  for working capital in small- and middle-scale business.The population of this study was all the small- and middle-scale businesses in Lombok Island. Samples were collected by using judgment sampling of non probability sampling. The samples taken were the small- and middle-scale businesses of pottery industry which have exported their goods. The respondents were the managers and owners of small- and middle-scale businesses. The method of data analysis employed in this study was Generalized Structured Component Analysis.                The finding of this study showed that  mental accounting is rational by nature and have significant effects on company performance through placement of funds for working capital in small-and middle-scale business.This phenomenon is grounded from the fact that most managers and owners of those small- and middle-scale businesses are men who are on their productive age, and have more than 10 years of experience.  The placement of funds for working capital is shown by the efficiency of cash, receivable  and inventory management which in turn improve the company performance. Keywords: mental accounting, working capital, company performance

scholarly journals Multigroup Moderation Test in Generalized Structured Component Analysis

CAUCHY ◽  
2016 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Angga Dwi Mulyanto ◽  
Solimun Solimun ◽  
Ni Wayan Surya Wardhani ◽  
Suharno Suharno
Keyword(s):  
Least Squares ◽  
Standard Error ◽  
Complex Analysis ◽  
Component Analysis ◽  
T Test ◽  
Free Software ◽  
Path Coefficient ◽  
Generalized Structured Component Analysis ◽  
Sample Size Estimate ◽  
Long Time

Generalized Structured Component Analysis (GSCA) is an alternative method in structural modeling using alternating least squares. GSCA can be used for the complex analysis including multigroup. GSCA can be run with a free software called GeSCA, but in GeSCA there is no multigroup moderation test to compare the effect between groups. In this research we propose to use the T test in PLS for testing moderation Multigroup on GSCA. T test only requires sample size, estimate path coefficient, and standard error of each group that are already available on the output of GeSCA and the formula is simple so the user does not need a long time for analysis.

scholarly journals Functional Generalized Structured Component Analysis

Psychometrika ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. 940-968 ◽  
Author(s):  
Hye Won Suk ◽  
Heungsun Hwang
Keyword(s):  
Component Analysis ◽  
Generalized Structured Component Analysis

Generalized structured component analysis

Psychometrika ◽  
2004 ◽  
Vol 69 (1) ◽  
pp. 81-99 ◽  
Author(s):  
Heungsun Hwang ◽  
Yoshio Takane
Keyword(s):  
Component Analysis ◽  
Generalized Structured Component Analysis

scholarly journals Fuzzy Clusterwise Generalized Structured Component Analysis

Psychometrika ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. 181-198 ◽  
Author(s):  
Heungsun Hwang ◽  
Wayne S. Desarbo ◽  
Yoshio Takane
Keyword(s):  
Component Analysis ◽  
Generalized Structured Component Analysis

scholarly journals Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2076
Author(s):  
Seohee Park ◽  
Seongeun Kim ◽  
Ji Hoon Ryoo
Keyword(s):  
Latent Class ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Component Analysis ◽  
Small Sample ◽  
Multivariate Normality ◽  
Generalized Structured Component Analysis ◽  
Latent Class Regression ◽  
Normality Assumption ◽  
Fuzzy Clustering Method

Latent class analysis (LCA) has been applied in many research areas to disentangle the heterogeneity of a population. Despite its popularity, its estimation method is limited to maximum likelihood estimation (MLE), which requires large samples to satisfy both the multivariate normality assumption and local independence assumption. Although many suggestions regarding adequate sample sizes were proposed, researchers continue to apply LCA with relatively smaller samples. When covariates are involved, the estimation issue is encountered more. In this study, we suggest a different estimating approach for LCA with covariates, also known as latent class regression (LCR), using a fuzzy clustering method and generalized structured component analysis (GSCA). This new approach is free from the distributional assumption and stable in estimating parameters. Parallel to the three-step approach used in the MLE-based LCA, we extend an algorithm of fuzzy clusterwise GSCA into LCR. This proposed algorithm has been demonstrated with an empirical data with both categorical and continuous covariates. Because the proposed algorithm can be used for a relatively small sample in LCR without requiring a multivariate normality assumption, the new algorithm is more applicable to social, behavioral, and health sciences.

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.

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