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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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The perceptions of social media users of digital detox apps considering personality traits

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EDUCATION AND INFORMATION TECHNOLOGIES 2022年第7期27卷 9293-9316页

作者： Nguyen, Vinh T. TNU Univ Informat & Commun Technol Thai Nguyen Vietnam FPT Univ Hanoi Vietnam

The purpose of this study was to investigate the perceptions of users about using digital detox applications and to display relationships among personality traits and technology-related variables. This study was designed using survey approach and employed generalized structured component analysis (GSCA). As such, 11 hypotheses were constructed and tested. The study recruited 263 participants who utilize detox applications to avoid social media distractions. Data were collected through Google Form and analyzed using GSCA Pro 1.1 to better understand whether the proposed conceptual model fits the data. The results of the study indicated that behavioral intention predicted usage behavior significantly;performance expectancy, effort expectancy, and social influence positively affected behavioral intention;in turn, agreeableness and extroversion positively influenced performance expectancy, and extroversion affected effort expectancy;finally, neuroticism had a statistically significant and negatively associated with effort expectancy of using social media detox apps. The significant exceptions were that facilitating conditions were not predictive of behavioral intention, openness to experience did not influence performance expectancy, and conscientiousness was not linked to effort expectancy. The proposed conceptual model explained 56.68% of the amount of variation, indicating that instructors, policy makers and software designers should consider personal factors for preparing practical intervention approaches to mitigate learning issues related to social media distraction.

关键词： Personality traits Social media detox apps generalized structured component analysis Covid-19 Education issues utaut

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Mobile Operating Systems' Impact on Customer Value: IOS vs. Android

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JOURNAL OF COMPUTER INFORMATION SYSTEMS 2023年

作者： Xun, Jiyao Chong, Woon Kian Dolega, Les Xian Jiaotong Liverpool Univ Entrepreneur Coll Entrepreneurship & Enterprise Hub Taicang City Peoples R China SP Jain Sch Global Management Dept Mkt Singapore Singapore Univ Liverpool Dept Geog & Planning Liverpool England SP Jain Sch Global Management Dept Mkt 10 Hyderabad Rd Singapore 119579 Singapore

Amidst the growing focus on media engagement and customer value in retail marketing literature, mobile commerce (MC) research has gained prominence. This research explores how customers employ mobile operating systems to engage with retailers and extract value within the context of Fast Moving Consumer Goods (FMCG) in retail. In Study 1, a survey involving 398 users uncovered that the customer handset OS moderates the effects of social media, traditional media engagement, and retail "place" on customer value. In Study 2, leveraging data from a foreign FMCG brand deeply immersed in social media platforms, we scrutinize how such engagement dynamics affect the influence of "place" on product sales across e-commerce and conventional retail channels. Our findings make significant theoretical contributions to comprehending customer value in MC, with practical implications for marketers, emphasizing the potential of customer mobile operating system as a valuable tool for effective marketing strategies.

关键词： Social media customer value mobile commerce mobile operating systems generalized structured component analysis

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generalized structured component analysis with Latent Interactions

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PSYCHOMETRIKA 2010年第2期75卷 228-242页

作者： Hwang, Heungsun Ho, Moon-Ho Ringo Lee, Jonathan McGill Univ Montreal PQ H3A 2T5 Canada Nanyang Technol Univ Singapore 639798 Singapore

generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA is proposed to effectively deal with various types of interactions among latent variables. In the proposed method, a latent interaction is defined as a product of interacting latent variables. As a result, this method does not require the construction of additional indicators for latent interactions. Moreover, it can easily accommodate both exogenous and endogenous latent interactions. An alternating least-squares algorithm is developed to minimize a single optimization criterion for parameter estimation. A Monte Carlo simulation study is conducted to investigate the parameter recovery capability of the proposed method. An application is also presented to demonstrate the empirical usefulness of the proposed method.

关键词： generalized structured component analysis latent interactions alternating least squares

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Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes

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BMC BIOINFORMATICS 2018年第Sup4期19卷 79-79页

作者： Lee, Sungyoung Kim, Yongkang Choi, Sungkyoung Hwang, Heungsun Park, Taesung Seoul Natl Univ Interdisciplinary Program Bioinformat Seoul South Korea Seoul Natl Univ Dept Stat 1 Gwanak Ro Gwanak Gu Seoul 08826 South Korea McGill Univ Dept Psychol Montreal PQ Canada

Background: As one possible solution to the "missing heritability" problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. Results: Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset. Conclusions: Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously.

关键词： Pathway-based analysis Next-generation sequencing data Multivariate analysis generalized structured component analysis Hierarchical analysis

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HisCoM-GGI: Hierarchical structural component analysis of gene-gene interactions

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JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 2018年第6期16卷 1840026页

作者： Choi, Sungkyoung Lee, Sungyoung Kim, Yongkang Hwang, Heungsun Park, Taesung Yonsei Univ Coll Med Dept Pharmacol 50-1 Yonsei Ro Seoul 03722 South Korea Seoul Natl Univ Hosp Ctr Precis Med 71 Daehak Ro Seoul 03082 South Korea Seoul Natl Univ Dept Stat 1 Gwanak Ro Seoul 08826 South Korea McGill Univ Dept Psychol 2001 Ave McGill Coll Montreal PQ H3A 1G1 Canada Seoul Natl Univ Interdisciplinary Program Bioinformat 1 Gwanak Ro Seoul 08826 South Korea

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene-gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural component analysis of Gene-Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP-SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 x SPOCK1) and (LINGO2 x ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand bi

关键词： Genome-wide association study gene-gene interactions generalized structured component analysis ridge regression

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Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods

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JOURNAL OF THE ACADEMY OF MARKETING SCIENCE 2017年第5期45卷 616-632页

作者： Hair, Joseph F. Hult, G. Tomas M. Ringle, Christian M. Sarstedt, Marko Thiele, Kai Oliver Univ S Alabama Mobile AL USA Michigan State Univ E Lansing MI 48824 USA Hamburg Univ Technol TUHH Hamburg Germany Univ Newcastle Callaghan NSW Australia Otto von Guericke Univ Magdeburg Germany

Composite-based structural equation modeling (SEM), and especially partial least squares path modeling (PLS), has gained increasing dissemination in marketing. To fully exploit the potential of these methods, researchers must know about their relative performance and the settings that favor each method's use. While numerous simulation studies have aimed to evaluate the performance of composite-based SEM methods, practically all of them defined populations using common factor models, thereby assessing the methods on erroneous grounds. This study is the first to offer a comprehensive assessment of composite-based SEM techniques on the basis of composite model data, considering a broad range of model constellations. Results of a large-scale simulation study substantiate that PLS and generalized structured component analysis are consistent estimators when the underlying population is composite model-based. While both methods outperform sum scores regression in terms of parameter recovery, PLS achieves slightly greater statistical power.

关键词： Composite generalized structured component analysis GSCA Partial least squares PLS SEM Simulation Structural equation modeling Sum scores regression

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Regularized generalized structured component analysis

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PSYCHOMETRIKA 2009年第3期74卷 517-530页

作者： Hwang, Heungsun McGill Univ Dept Psychol Montreal PQ H3A 1B1 Canada

generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge type of regularization into GSCA in a unified framework, thereby enabling to handle multi-collinearity problems effectively. An alternating regularized least squares algorithm is developed for parameter estimation. A Monte Carlo simulation study is conducted to investigate the performance of the proposed method as compared to its non-regularized counterpart. An application is also presented to demonstrate the empirical usefulness of the proposed method.

关键词： generalized structured component analysis multi-collinearity ridge-type regularization alternating regularized least squares

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Pathway-Based Association Study of Multiple Candidate Genes and Multiple Traits Using Structural Equation Models

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GENETIC EPIDEMIOLOGY 2015年第2期39卷 101-113页

作者： Romdhani, Hela Hwang, Heungsun Paradis, Gilles Roy-Gagnon, Marie-Helene Labbe, Aurelie McGill Univ Dept Epidemiol Biostat & Occupat Hlth Montreal PQ Canada McGill Univ Dept Psychol Montreal PQ Canada McGill Univ Hlth Ctr Res Inst Montreal PQ Canada Inst Natl Sante Publ Quebec Montreal PQ Canada Univ Ottawa Dept Epidemiol & Community Med Ottawa ON Canada McGill Univ Douglas Mental Hlth Univ Inst Montreal PQ Canada McGill Univ Dept Psychiat Montreal PQ Canada

There is increasing interest in the joint analysis of multiple genetic variants from multiple genes and multiple correlated quantitative traits in association studies. The classical approach involves testing univariate associations between genotypes and phenotypes and correcting for multiple testing that results in loss of power to detect associations. In this paper, we propose modeling complex relationships between genetic variants in candidate genes and measured correlated traits using structural equation models (SEM), taking advantage of prior knowledge on clinical and genetic pathways. We adopt generalized structured component analysis (GSCA) as an approach to SEM and develop a single association test between multiple genetic variants in a gene and a set of correlated traits, taking into account all available data from other genes and other traits. The performance of this test is investigated by simulations. We apply the proposed method to the Quebec Child and Adolescent Health and Social Survey (1999) data to investigate genetic associations with cardiovascular disease-related traits.

关键词： generalized structured component analysis prior biological knowledge clinical pathway genetic pathway permutation test

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