An Introduction to Structural Equation Modeling

AbstractStructural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). Whereas CB-SEM is primarily used to confirm theories, PLS represents a causal–predictive approach to SEM that emphasizes prediction in estimating models, whose structures are designed to provide causal explanations. PLS-SEM is also useful for confirming measurement models. This chapter offers a concise overview of PLS-SEM’s key characteristics and discusses the main differences compared to CB-SEM. The chapter also describes considerations when using PLS-SEM and highlights situations that favor its use compared to CB-SEM.

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EFFECTS OF E-COMMERCE ON THE SHARIA HOTELS BRAND AWARENESS IN BANDUNG

Advances in Social Sciences Research Journal ◽

10.14738/assrj.510.5338 ◽

2018 ◽

Vol 5 (10) ◽

Author(s):

Muchyidin Natakusuma ◽

Ria Arifianti ◽

Iwan Sukoco

Keyword(s):

Structural Equation Modeling ◽

Significant Influence ◽

Structural Equation ◽

Internet Technology ◽

Brand Awareness ◽

Equation Modeling ◽

The Internet ◽

Boutique Hotel ◽

Complex Relationships ◽

A Company

Sharia has become global, including the development of Sharia Hotels. In addition, the development of internet technology in Indonesia can be seen with the existence of e-commerce which benefits in the form of business convenience because the internet is seen as an advertising media that is cheaper, more efficient and effective. Tight business competition has made brand as an important aspect that can be a benchmark for a company. Sharia hotel owners use e-commerce to build strong brands and make consumers acknowledge that their brands are well received by consumers. This study aims to find out how e-commerce influences brand awareness in Narapati Indah Sharia Boutique Hotel and Convention Bandung. Respondents in this study were sampled using the Structural Equation Modeling (SEM) method that allows testing a series of relatively complex relationships simultaneously. The results showed that respondents' responses regarding e-commerce and brand awareness at Narapati Indah Sharia Hotel were in the good category. In addition, there is a significant influence between e-commerce on brand awareness in Narapati Indah Sharia Hotel especially through their own website so that to depend on other parties are no longer needed.

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Evaluating the Construct Validity of Ryff’s Scales of Psychological Well-Being Using Exploratory Structural Equation Modeling

Journal of Psychoeducational Assessment ◽

10.1177/0734282916652756 ◽

2016 ◽

Vol 35 (6) ◽

pp. 633-638 ◽

Cited By ~ 1

Author(s):

Hsien-Yuan Hsu ◽

Tze-Li Hsu ◽

KoFan Lee ◽

Lori Wolff

Keyword(s):

Structural Equation Modeling ◽

Construct Validity ◽

Structural Equation ◽

Discriminant Validity ◽

Well Being ◽

The United States ◽

Equation Modeling ◽

Exploratory Structural Equation Modeling ◽

Psychological Well Being ◽

Measurement Models

The purpose of this study was to evaluate the construct validity of Ryff’s Scales of Psychological Well-Being (SPWB) using exploratory structural equation modeling (ESEM). The data were drawn from the national survey of Midlife in the United States conducted during 1994 and 1995. Measurement models assuming different number of factors (1-6 factors) and considering the effect of negatively wording items were specified and compared to determine optimal number of underlying factors. The discriminant validity was assessed following Farrell’s suggestions. The results showed the discriminant validity was questionable due to five indicators with considerable cross-loadings.

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Constructing Measurement Models of L2 Linguistic Complexity: A Structural Equation Modeling Approach

JLTA Journal ◽

10.20622/jltajournal.22.0_23 ◽

2019 ◽

Vol 22 (0) ◽

pp. 23-43

Author(s):

Takeshi KATO

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Equation Modeling ◽

Linguistic Complexity ◽

Measurement Models ◽

Modeling Approach

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A 'Weight of Evidence' approach to evaluating structural equation models

One Ecosystem ◽

10.3897/oneeco.5.e50452 ◽

2020 ◽

Vol 5 ◽

Cited By ~ 1

Author(s):

James Grace

Keyword(s):

Structural Equation Modeling ◽

Model Selection ◽

Model Evaluation ◽

Structural Equation ◽

Structural Equation Models ◽

Weight Of Evidence ◽

Equation Modeling ◽

Future Studies ◽

Current State ◽

History Of

It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.

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Structural equation models - PLS in engineering sciences: a brief guide for researchers through a case applied to the industry

Athenea ◽

10.47460/athenea.v2i4.17 ◽

2021 ◽

Vol 2 (4) ◽

pp. 5-18

Author(s):

Juan Enrique Villalva A.

Keyword(s):

Structural Equation Modeling ◽

Least Squares ◽

Partial Least Squares ◽

Structural Equation ◽

Structural Equation Models ◽

Structural Equations ◽

Partial Least Square ◽

Equation Modeling ◽

Engineering Sciences ◽

The Impact

Modeling using structural equations, is a second generation statistical data analysis technique, it has been positioned as the methodological options most used by researchers in various fields of science. The best known method is the covariance-based approach, but it presents some limitations for its application in certain cases. Another alternative method is based on the variance structure, through the analysis of partial least squares, which is an appropriate option when the research involves the use of latent variables (for example, composite indicators) prepared by the researcher, and where it is necessary to explain and predict complex models. This article presents a brief summary of the structural equation modeling technique, with an example on the relationship of constructs, sustainability and competitiveness in iron mining, and is intended to be a brief guide for future researchers in the engineering sciences. Keywords: Competitiveness, Structural equations, Iron mining, Sustainability. References [1]J. Hair, G. Hult, C. Ringle and M. Sarstedt. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). California: United States. Sage, 2017. [2]H. Wold. Model Construction and Evaluation when Theoretical Knowledge Is Scarce: An Example of the Use of Partial Least Squares. Genève. Faculté des Sciences Économiques et Sociales, Université de Genève. 1979. [3]J. Henseler, G. Hubona & P. Ray. “Using PLS path modeling new technology research: updated guidelines”. Industrial Management & Data Systems, 116(1), 2-20. 2016. [4]G. Cepeda and Roldán J. “Aplicando en la Práctica la Técnica PLS en la Administración de Empresas”. Congreso de la ACEDE, Murcia, España, 2004. [5]D. Garson. Partial Least Squares. Regresión and Structural Equation Models. USA. Statistical Associates Publishing: 2016. [6]D. Barclay, C. Higgins & R. Thompson. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration”. Technology Studies. Special Issue on Research Methodology. (2:2), pp. 285-309. 1995. [7]J. Medina, N. Pedraza & M. Guerrero. “Modelado de Ecuaciones Estructurales. Un Enfoque de Partial Least Square Aplicado en las Ciencias Sociales y Administrativas”. XIV Congreso Internacional de la Academia de Ciencias Administrativas A.C. (ACACIA). EGADE – ITESM. Monterrey, México, 2010. [8]J. Medina & J. Chaparro. “The Impact of the Human Element in the Information Systems Quality for Decision Making and User Satisfaction”. Journal of Computer Information Systems. (48:2), pp. 44-52. 2008. [9]D. Leidner, S. Carlsson, J. Elam & M. Corrales. “Mexican and Swedish Managers’ Perceptions of the Impact of EIS on Organizational Intelligence, Decisión Making, and Structure”. Decision Science. (30:3), pp. 633-658. 1999.[10]W. Chin. “The partial least squares approach for structural equation modeling”. Chapter Ten, pp. 295-336 in Modern methods for business research. Edited by Macoulides, G. A., New Jersey: Lawrence Erlbaum Associates, 1998. [11]M. Höck & C. Ringle M. “Strategic networks in the software industry: An empirical analysis of the value continuum”. IFSAM VIIIth World Congress, Berlin 2006. [12]J. Henseler, Ch. Ringle & M. Sarstedt. Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer, 2012. [13]S. Daskalakis & J. Mantas. “Evaluating the impact of a service-oriented framework for healthcare interoperability”. Studies in Health Technology and Informatics. pp. 285-290. 2008. [14]C. Fornell & D. Larcker: “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, vol. 18, pp. 39-50. Februay 1981. [15]C. Fornell. A Second Generation of Multivariate Analysis: An Overview. Vol. 1. New York, U.S.A. Praeger Publishers: 1982. [16]R. Falk and N. Miller. A Primer for Soft Modeling. Ohio: The University of Akron. 1992. [17]M. Martínez. Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. Revista Iberoamericana para Investigación y el Desarrollo Educativo. Vol. 8, Núm. 16. 2018. [18]S. Geisser. “A predictive approach to the random effects model”. Biometrika, Vol. 61(1), pp. 101-107. 1974. [19]J. Cohen. Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. [20]GRI (2013). G4 Sustainability Reporting Guidelines. Global Reporting Initiative. Available: www.globalreporting.org

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PLS-SEM STATISTICAL PROGRAMS: A REVIEW

Journal of Applied Structural Equation Modeling ◽

10.47263/jasem.5(1)06 ◽

2021 ◽

Vol 5 (1) ◽

pp. i-xiv

Author(s):

Mumtaz Ali Memon ◽

T. Ramayah ◽

Jun-Hwa Cheah ◽

Hiram Ting ◽

Francis Chuah ◽

...

Keyword(s):

Structural Equation Modeling ◽

Data Analysis ◽

Least Squares ◽

Partial Least Squares ◽

Multivariate Data Analysis ◽

Structural Equation ◽

Multivariate Data ◽

Informed Decision ◽

Equation Modeling

Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of PLS-SEM, little is done to explain the attributes of the different PLS-SEM statistical applications. The objective of this editorial is to discuss a variety of PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial can serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make an informed decision on the most appropriate application in their research.

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Analyzing factorial survey data with structural equation models

Sociological Methods & Research ◽

10.1177/00491241211043139 ◽

2021 ◽

pp. 004912412110431

Author(s):

Bert Weijters ◽

Eldad Davidov ◽

Hans Baumgartner

Keyword(s):

Structural Equation Modeling ◽

Survey Data ◽

Structural Equation ◽

Multilevel Models ◽

Structural Equation Models ◽

Social Science Research ◽

Science Research ◽

Equation Modeling ◽

Factorial Survey ◽

Modeling Framework

In factorial survey designs, respondents evaluate multiple short descriptions of social objects (vignettes) that experimentally vary different levels of attributes of interest. Analytical methods (including individual-level regression analysis and multilevel models) estimate the weights (or utilities) assigned to the levels of the different attributes by participants to arrive at an overall response to the vignettes. In the current paper, we explain how data from factorial surveys can be analyzed in a structural equation modeling framework using an approach called structural equation modeling for within-subject experiments. We review the use of factorial surveys in social science research, discuss typically used methods to analyze factorial survey data, introduce the structural equation modeling for within-subject experiments approach, and present an empirical illustration of the proposed method. We conclude by describing several extensions, providing some practical recommendations, and discussing potential limitations.

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Structural Equation Modeling (SEM)-Based Meta-Analysis

10.31234/osf.io/93nfr ◽

2021 ◽

Author(s):

Mike W.-L. Cheung

Keyword(s):

Social Sciences ◽

Structural Equation Modeling ◽

Structural Equation ◽

Structural Equation Models ◽

Meta Analysis ◽

Equation Model ◽

Equation Modeling ◽

Future Directions ◽

Software Packages ◽

Meta Analyses

Structural equation modeling (SEM) and meta-analysis are two popular techniques in the behavioral, medical, and social sciences. They have their own research communities, terminologies, models, software packages, and even journals. This chapter introduces SEM-based meta-analysis, an approach to conduct meta-analyses using the SEM framework. By conceptualizing studies in a meta-analysis as subjects in a structural equation model, univariate, multivariate, and three-level meta-analyses can be fitted as structural equation models using definition variables. We will review fixed-, random-, and mixed-effects models using the SEM framework. Examples will be used to illustrate the procedures using the metaSEM and OpenMx packages in R. This chapter closes with a discussion of some future directions for research.

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Meta-Analytic Structural Equation Modeling

Oxford Research Encyclopedia of Business and Management ◽

10.1093/acrefore/9780190224851.013.225 ◽

2021 ◽

Author(s):

Mike W.-L. Cheung

Keyword(s):

Structural Equation Modeling ◽

Correlation Matrix ◽

Structural Equation ◽

Structural Equation Models ◽

Meta Analysis ◽

Equation Modeling ◽

Average Correlation ◽

Correlation Matrices ◽

Factor Analytic Model ◽

Special Cases

Meta-analysis and structural equation modeling (SEM) are two popular statistical models in the social, behavioral, and management sciences. Meta-analysis summarizes research findings to provide an estimate of the average effect and its heterogeneity. When there is moderate to high heterogeneity, moderators such as study characteristics may be used to explain the heterogeneity in the data. On the other hand, SEM includes several special cases, including the general linear model, path model, and confirmatory factor analytic model. SEM allows researchers to test hypothetical models with empirical data. Meta-analytic structural equation modeling (MASEM) is a statistical approach combining the advantages of both meta-analysis and SEM for fitting structural equation models on a pool of correlation matrices. There are usually two stages in the analyses. In the first stage of analysis, a pool of correlation matrices is combined to form an average correlation matrix. In the second stage of analysis, proposed structural equation models are tested against the average correlation matrix. MASEM enables researchers to synthesize researching findings using SEM as the research tool in primary studies. There are several popular approaches to conduct MASEM, including the univariate-r, generalized least squares, two-stage SEM (TSSEM), and one-stage MASEM (OSMASEM). MASEM helps to answer the following key research questions: (a) Are the correlation matrices homogeneous? (b) Do the proposed models fit the data? (c) Are there moderators that can be used to explain the heterogeneity of the correlation matrices? The MASEM framework has also been expanded to analyze large datasets or big data with or without the raw data.

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