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What is Structural Equation Modeling

Handbook of Research on Contemporary Theoretical Models in Information Systems
Multivariate technique combining aspects of multiple regression (examining dependence relationships) and factor analysis (representing unmeasured concepts with multiple variables) to estimate a series of interrelated dependence relationships simultaneously
Published in Chapter:
Theory Development in Information Systems Research Using Structural Equation Modeling: Evaluation and Recommendations
Nicholas Roberts (Clemson University, USA) and Varun Grover (Clemson University, USA)
DOI: 10.4018/978-1-60566-659-4.ch005
Abstract
Structural equation modeling (SEM) techniques have significant potential for assessing and modifying theoretical models. There have been 171 applications of SEM in IS research, published in major journals, most of which have been after 1994. Despite SEM’s surging popularity in the IS field, it remains a complex tool that is often mechanically used but difficult to effectively apply. The purpose of this study is to review previous applications of SEM in IS research and to recommend guidelines to enhance the use of SEM to facilitate theory development. The authors review and evaluate SEM applications, both component-based (e.g., PLS) and covariance-based (e.g., LISREL), according to prescribed criteria. Areas of improvement are suggested which can assist application of this powerful technique in IS theory development.
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More Results
Consumer Values and Green Products Consumption in Malaysia: A Structural Equation Modelling Approach
Structural equation modeling (SEM) as a second-generation technique, is a statistical technique for building and testing simultaneous modeling of relationships among multiple variables.
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The Effect of the Festivals on the Development of Tourism With the Support of Residents: The Case Study of Zeytinli Rockfest in Edremit
Structural equation modeling is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to analyze the structural relationship between measured variables and latent constructs.
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Using Tests to Study People's Responses: What Do the Scores Mean?
Set of techniques used to assess the relationships between observed (items) and unobserved (factors) variables. This methodology is popular in behavioural and health sciences.
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The Impact of Human Values and Knowledge on Green Products Purchase Intention
Structural equation modeling (SEM) as a second-generation technique, is a statistical technique for building and testing simultaneous modeling of relationships among multiple variables.
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A Survey on the Adoption and Usage of Broadband Internet
Structural equation modeling, an integral tool in both managerial and academic research, is a statistical method useful when one dependent variable becomes an independent variable in subsequent dependence relationships (Hair et al., 1995). It provides a straightforward method of dealing with multiple relationships simultaneously while providing statistical efficiency. All structural equation modeling techniques are distinguished by their ability to estimate multiple and interrelated dependence relationships and account measurement error at the same time. It is an important tool for studying causal models (Fornell, 1982; Fornell et al., 1981).
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Molding Me in Their Image
A multivariate statistical technique that combines multiple regression analysis and factor analysis to analyze structural relationships.
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Understanding the Antecedents of Customer Loyalty by Applying Structural Equation Modeling
A complicated analysis that envelopes and uses the concepts of regression analysis, path analysis and confirmatory factor analysis, which they can be regarded as special types of SEM. However, SEM is implied as confirmatory, rather than data driven exploratory modeling; thus, it is appropriate for model testing, rather than model development. SEM comprises of two components, a measurement model and a structural model.
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Inferring Gene Regulatory Networks from Genetical Genomics Data
Structural Equation Modeling is a linear statistical modeling framework for testing and estimating causal relationships among variables. It has been widely used in econometrics, sociology and psychology, usually as a confirmatory procedure instead of an exploratory analysis for causal inference.
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