Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error

Hwang, Heungsun; Takane, Yoshio; Jung, Kwanghee

doi:10.3389/fpsyg.2017.02137

Cited by 60 publications

(58 citation statements)

References 41 publications

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“…In a simulation study, rGSCA was found to provide parameter estimates that are as good as or better than those from original GSCA in various conditions of normally distributed data. Furthermore, we may also consider a simulation study for comparisons of the CI methods in GSCA with uniqueness terms for accommodating measurement error (GSCA M ; Hwang et al, 2017), which has been proposed to extend the original GSCA to account for errors in indicators explicitly. This extension contemplates both common and unique parts of indicators and estimates a weighted composite of indicators with their unique parts removed.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

et al. 2019

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Generalized structured component analysis (GSCA) is a theoretically well-founded approach to component-based structural equation modeling (SEM). This approach utilizes the bootstrap method to estimate the confidence intervals of its parameter estimates without recourse to distributional assumptions, such as multivariate normality. It currently provides the bootstrap percentile confidence intervals only. Recently, the potential usefulness of the bias-corrected and accelerated bootstrap (BCa) confidence intervals (CIs) over the percentile method has attracted attention for another component-based SEM approach—partial least squares path modeling. Thus, in this study, we implemented the BCa CI method into GSCA and conducted a rigorous simulation to evaluate the performance of three bootstrap CI methods, including percentile, BCa, and Student's t methods, in terms of coverage and balance. We found that the percentile method produced CIs closer to the desired level of coverage than the other methods, while the BCa method was less prone to imbalance than the other two methods. Study findings and implications are discussed, as well as limitations and directions for future research.

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Section: Discussionmentioning

confidence: 99%

Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

et al. 2019

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“…Table 2, it can be seen the known FIT value of 0.6544, Adjusted FIT (AFIT) of 0.6467, Goodness-of-Fit Index (GFI) of 0.9984, and Standardized Root Mean Square Residual (SRMR) of 0.0691. The FIT value (ranging from 0 to 1) explains the total variance of all variables that can be explained by a particular model (Hwang et al, 2017). FIT value of 0.6544 means that the research model is good enough to explain the studied phenomenon.…”

Section: Finding and Discussionmentioning

confidence: 99%

“…The data from the dissemination were then processed with General Structured Component Analysis (GSCA) which is an analysis tool in the Structural Equation Modeling (SEM) analysis method. The output of the GSCA is a measure of fit in the measurement model (including validity and reliability testings), structural models, and overall models (Hwang et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Implication of Future Time Perspective and Hope on the Continuance Commitment of Hotel Employees

Oktavio

Kaihatu

2020

JECO

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In the Indonesian hotel industry, high employee turnover rates are very common costly. One of the reasons that causes hotel employee turnover to be high is low commitment. The low commitment is triggered by the low hope of hotel employees to remain and to be committed to continuing their careers in the hotel where they are working today. Employees need certainty about their future that should be guaranteed by hotel management. This study tries to analyze the role of Future Time Perspective (FTP) and its effect on hope and continuance commitment. The data were derived from distributing questionnaires to hotel employees spread in 45 three-star hotels in Surabaya, and subsequently analyzed using the General Structured Component Analysis (GSCA) tool. The results show that FTP influences hope and continuance commitment significantly. Similarly, hope has a significant effect on continuance commitment. Hope is also able to act as a mediator between FTP and continuance commitment.Keywords: hotel industry, future time perspective, hope, continuance commitment Implikasi Future Time Perspective dan Harapan Terhadap Continuance Commitment Karyawan HotelAbstrak: Di industri perhotelan Indonesia, tingkat turnover karyawan yang tinggi sudah menjadi rahasia umum dan sangat memakan biaya. Salah satu alasan yang menyebabkan tingginya tingkat turnover karyawan hotel adalah komitmen yang rendah. Komitmen yang rendah tersebut dipicu oleh rendahnya tingkat harapan karyawan hotel untuk tetap tinggal serta berkomitmen untuk melanjutkan karir di hotel tempat mereka bekerja saat ini. Karyawan hotel membutuhkan kepastian akan masa depannya yang seharusnya bisa dijamin oleh pihak manajemen hotel. Penelitian ini mencoba menganalisa peran Future Time perspective (FTP) dan pengaruhnya terhadap harapan dan continuance commitment karyawan hotel. Data penelitian berasal dari penyebaran kuesioner kepada karyawan hotel yang tersebar di 45 hotel bintang tiga di Surabaya dan selanjutnya dianalisis menggunakan alat analisis General Structured Component Analysis (GSCA). Hasilnya menunjukkan bahwa FTP mempengaruhi harapan dan continuance commitment secara signifikan. Harapan juga berpengaruh secara signifikan terhadap continuance commitment. Harapan juga berperan sebagai mediator antara FTP dan continuance commitment.Kata kunci: industri perhotelan, future time perspective, harapan, continuance commitment

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“…This is preferable to using the original observed data themselves, particularly when the observed data are contaminated by large random errors. Also, such specification makes it possible to address the common criticism of component‐based SEM, including the original GSCA, that there is no formal way of accounting for measurement errors in observed variables (Bentler & Huang, ; Hwang, Takane, & Jung, ). Hence, compared to the original GSCA, BGSCA allows one to specify parameters associated with measurement error terms (i.e., variances of the error terms) as an additional set of parameters to be estimated.…”

Section: Introductionmentioning

confidence: 99%

Bayesian generalized structured component analysis

Choi

Hwang

2019

Brit J Math & Statis

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Generalized structured component analysis (GSCA) is a component-based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA, called BGSCA, is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA. We conduct a simulation study to evaluate the performance of BGSCA. We also apply BGSCA to real data to demonstrate its empirical usefulness.

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Generalized Structured Component Analysis with Uniqueness Terms for Accommodating Measurement Error

Cited by 60 publications

References 41 publications

Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation

Implication of Future Time Perspective and Hope on the Continuance Commitment of Hotel Employees

Bayesian generalized structured component analysis

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