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Three-Way generalized structured component analysis 1

82nd Annual Meeting of the Psychometric-Society

作者： Choi, Ji Yeh Yang, Seungmi Tenenhaus, Arthur Hwang, Heungsun Natl Univ Singapore 9 Arts Link Singapore Singapore McGill Univ 1020 Pine Ave West Montreal PQ Canada Univ Paris Saclay Univ Paris Sud CNRS Lab Signaux & Syst L2SCent Supelec 3 Rue Joliot Curie F-91192 Gif Sur Yvette France McGill Univ 2001 McGill Coll Montreal PQ Canada

ISBN: (数字)9783319772493

ISBN: (纸本)9783319772493;9783319772486

generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling, where components of observed variables are used as proxies for latent variables. GSCA has thus far focused on analyzing two-way (e.g., subjects by variables) data. In this paper, GSCA is extended to deal with three-way data that contain three different types of entities (e.g., subjects, variables, and occasions) simultaneously. The proposed method, called three-way GSCA, permits each latent variable to be loaded on two types of entities, such as variables and occasions, in the measurement model. This enables to investigate how these entities are associated with the latent variable. The method aims to minimize a single least squares criterion to estimate parameters. An alternating least squares algorithm is developed to minimize this criterion. We conduct a simulation study to evaluate the performance of three-way GSCA. We also apply three-way GSCA to real data to demonstrate its empirical usefulness.

关键词： generalized structured component analysis Three-way data Structural equation modeling Alternating least squares

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The Determination of Website Usability components by using generalized structured component analysis 10

The Determination of Website Usability Components by using G...

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10th Annual International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Sari, Rezki Wulan Permata Rochimah, Siti Sepuluh Nopember Inst Technol Dept Informat Technol & Commun Surabaya Indonesia

ISBN: (纸本)9781538647394

Website is a bridge between users and online information. Websites with high usability value of course will be accessed by many users. Therefore building a useful website is important. Of course there are components that affect the usability of the website. However, the list of usability components is not defined consistently in different standards or models. This causes confusion as there are many lists of available usability components. This study aims to determine the usability components on the website. This is done by validating the website usability components by looking at the effect of each attribute on usability using the generalized structured component analysis (GeSCA) method, and developing the usability component by adding the usability components of ISO/IEC 25023:2016. The data used are 3000 and collected by spreading questionnaire directly. The results of this study indicate that the utility component in the four approaches and components of usefulness is valid and positive, or in other words has a direct effect on usability with a 95 percent confidence interval.

关键词： determination usability components usability component validation websites usability ISO/IEC 25023:2016 generalized structured component analysis

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Using R Package gesca for generalized structured component analysis

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Behaviormetrika 2017年第1期44卷 3-23页

作者： Kim, Sunmee Cardwell, Ramsey Hwang, Heungsun Department of Psychology McGill University 1205 Dr. Penfield Avenue Montreal H3A 1B1 QC Canada

The R package gesca was recently released to implement generalized structured component analysis (GSCA). GSCA represents a component-based approach to structural equation modeling (SEM) that defines a latent variable as a component or weighted composite of indicators. gesca enables users to obtain overall and local measures of model fit, parameter estimates with bootstrapped standard errors and confidence intervals, and the total and indirect effects of latent variables and indicators. It can also implement several basic extensions of GSCA, including constrained single- and multiple-group analysis, and second-order latent variable modeling. Furthermore, users easily specify their hypothesized relationships among latent variables and/or indicators based on an intuitive text-based syntax that comprises indicator names and simple numerical operators. Owing to its analytic versatility and ease of use, the package can be attractive to those wishing to apply GSCA to their research. This article provides step-by-step guidance on using the package with real examples. © 2016, The Behaviormetric Society.

关键词： generalized structured component analysis R package Structural equation modeling

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Multilevel Dynamic generalized structured component analysis for Brain Connectivity analysis in Functional Neuroimaging Data

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PSYCHOMETRIKA 2016年第2期81卷 565-581页

作者： Jung, Kwanghee Takane, Yoshio Hwang, Heungsun Woodward, Todd S. Univ Texas Hlth Sci Ctr Houston Houston TX 77030 USA Univ Victoria Victoria BC V8W 2Y2 Canada McGill Univ Montreal PQ H3A 2T5 Canada Univ British Columbia Vancouver BC V5Z 1M9 Canada

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

关键词： generalized structured component analysis multilevel analysis structural equation modeling alternating least squares (ALS) algorithm brain connectivity analysis time series data functional neuroimaging multi-subject data

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Functional generalized structured component analysis

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PSYCHOMETRIKA 2016年第4期81卷 940-968页

作者： Suk, Hye Won Hwang, Heungsun Arizona State Univ Tempe AZ 85287 USA McGill Univ Montreal PQ Canada

An extension of generalized structured component analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.

关键词： generalized structured component analysis functional data analysis basis function expansion splines penalized least squares alternating least squares

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Understanding Composite-Based Structural Equation Modeling Methods From the Perspective of Regression component analysis

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MULTIVARIATE BEHAVIORAL RESEARCH 2024年第4期59卷 677-692页

作者： Rigdon, Edward E. Georgia State Univ Dept Mkt Atlanta GA USA Georgia State Univ Dept Mkt POB 3992 Atlanta GA 30302 USA

Regression component analysis (RCA) replaces the factors in a factor analysis model with weighted composites of the model's observed variables. The weight matrix may be calculated from the factor model's parameter estimates. Thus, RCA parameter estimates can be obtained using factor model software, but RCA composites have determinate scores, rather than the indeterminate scores of factors. Analytically, RCA equates to modeling with "regression method" factor scores, except that, while those scores will be inconsistent with the original factor model, they are strictly consistent with the RCA model. When the original factor model is strictly correct in the population and the composites in RCA are standardized, RCA parameter estimates replicate those from regression-weighted forms of partial least squares (PLS) path modeling and generalized structured component analysis (GSCA)-affirming that those methods also equate to modeling with regression method factor scores under the same conditions. Parallel measurement allows RCA to replicate both correlation weight and regression weight versions of PLS and GSCA. These results suggest that RCA and regression-weighted forms of PLS and GSCA are all consistent approaches for modeling data that conforms to a factor model. All analytical methods are described using one consistent symbol palette. Complete R syntax is provided.

关键词： Structural equation modeling partial least squares generalized structured component analysis regression component analysis composite-based methods

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Exploratory Procedure for component-Based Structural Equation Modeling for Simple Structure by Simultaneous Rotation

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PSYCHOMETRIKA 2024年第2期89卷 411-438页

作者： Yamashita, Naoto Kansai Univ Fac Sociol Osaka Japan

generalized structured component analysis (GSCA) is a structural equation modeling (SEM) procedure that constructs components by weighted sums of observed variables and confirmatorily examines their regressional relationship. The research proposes an exploratory version of GSCA, called exploratory GSCA (EGSCA). EGSCA is analogous to exploratory SEM (ESEM) developed as an exploratory factor-based SEM procedure, which seeks the relationships between the observed variables and the components by orthogonal rotation of the parameter matrices. The indeterminacy of orthogonal rotation in GSCA is first shown as a theoretical support of the proposed method. The whole EGSCA procedure is then presented, together with a new rotational algorithm specialized to EGSCA, which aims at simultaneous simplification of all parameter matrices. Two numerical simulation studies revealed that EGSCA with the following rotation successfully recovered the true values of the parameter matrices and was superior to the existing GSCA procedure. EGSCA was applied to two real datasets, and the model suggested by the EGSCA's result was shown to be better than the model proposed by previous research, which demonstrates the effectiveness of EGSCA in model exploration.

关键词： structural equation modeling generalized structured component analysis exploratory data analysis orthogonal rotation

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A comparative study of the predictive power of component-based approaches to structural equation modeling

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EUROPEAN JOURNAL OF MARKETING 2023年第6期57卷 1641-1661页

作者： Cho, Gyeongcheol Kim, Sunmee Lee, Jonathan Hwang, Heungsun Sarstedt, Marko Ringle, Christian M. McGill Univ Dept Psychol Montreal PQ Canada Univ Manitoba Dept Psychol Winnipeg MB Canada Univ La Verne Coll Business & Publ Management La Verne CA USA Ludwig Maximilian Univ Munich Munich Sch Management Munich Germany Babes Bolyai Univ Cluj Fac Econ & Business Adm Cluj Napoca Romania Hamburg Univ Technol TUHH Dept Management Sci & Technol Hamburg Germany

Purpose generalized structured component analysis (GSCA) and partial least squares path modeling (PLSPM) are two key component-based approaches to structural equation modeling that facilitate the analysis of theoretically established models in terms of both explanation and prediction. This study aims to offer a comparative evaluation of GSCA and PLSPM in a predictive modeling framework. Design/methodology/approach A simulation study compares the predictive performance of GSCA and PLSPM under various simulation conditions and different prediction types of correctly specified and misspecified models. Findings The results suggest that GSCA with reflective composite indicators (GSCA(R)) is the most versatile approach. For observed prediction, which uses the component scores to generate prediction for the indicators, GSCA(R) performs slightly better than PLSPM with mode A. For operative prediction, which considers all parameter estimates to generate predictions, both methods perform equally well. GSCA with formative composite indicators and PLSPM with mode B generally lag behind the other methods. Research limitations/implications Future research may further assess the methods' prediction precision, considering more experimental factors with a wider range of levels, including more extreme ones. Practical implications When prediction is the primary study aim, researchers should generally revert to GSCA(R), considering its performance for observed and operative prediction together. Originality/value This research is the first to compare the relative efficacy of GSCA and PLSPM in terms of predictive power.

关键词： Prediction Simulation Structural equation modeling generalized structured component analysis Partial least squares path modeling

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A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2022年第2期75卷 220-251页

作者： Cho, Gyeongcheol Sarstedt, Marko Hwang, Heungsun McGill Univ Montreal PQ Canada Ludwig Maximilians Univ Munchen Munich Germany Babes Bolyai Univ Cluj Napoca Romania

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches - the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM - under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.

关键词： construct misrepresentation factor score regression generalized structured component analysis maximum likelihood parameter recovery partial least squares path modelling population component models structural equation modelling

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

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JOURNAL OF THE ACADEMY OF MARKETING SCIENCE 2012年第3期40卷 402-413页

作者： Henseler, Jerg Radboud Univ Nijmegen Inst Management Res NL-6525 GD Nijmegen Netherlands Univ Nova Lisboa ISEGI P-1020312 Lisbon Portugal

generalized structured component analysis has emerged in marketing and psychometric literature as an alternative to structural equation modeling. A recent simulation study recommends that, in most cases, this analysis is preferable to structural equation modeling because it outperforms the latter when the model is misspecified. This article examines the characteristics of generalized structured component analysis and reveals that the surprising previous findings are attributable to an incomplete experimental design and an error incurred during the software implementation of generalized structured component analysis. Simulated data show that generalized structured component analysis provides inconsistent estimates. In some instances, model misspecification can nearly neutralize this inconsistency, but in others it will reinforce the inconsistency. Moreover, generalized structured component analysis is hardly suitable for mediation analysis because it substantially overestimates the direct effect. Thus, generalized structured component analysis cannot be recommended universally over structural equation modeling.

关键词： generalized structured component analysis Structural equation modeling Model misspecification Mediation

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