Model specification searches in structural equation modeling using tabu search

1998 ◽  
Vol 5 (4) ◽  
pp. 365-376 ◽  
Author(s):  
George A. Marcoulides ◽  
Zvi Drezner ◽  
Randall E. Schumacker
Keyword(s):  
Structural Equation Modeling ◽  
Tabu Search ◽  
Structural Equation ◽  
Model Specification ◽  
Equation Modeling

scholarly journals The use and misuse of structural equation modeling in management research

2014 ◽  
Vol 11 (1) ◽  
pp. 47-81 ◽  
Author(s):  
Nebojsa S. Davcik
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Conceptual Knowledge ◽  
Goodness Of Fit ◽  
Measurement Model ◽  
Model Specification ◽  
Equation Modeling ◽  
Research Practice ◽  
Management Research ◽  
Content Type

Purpose – The research practice in management research is dominantly based on structural equation modeling (SEM), but almost exclusively, and often misguidedly, on covariance-based SEM. The purpose of this paper is to question the current research myopia in management research, because the paper adumbrates theoretical foundations and guidance for the two SEM streams: covariance-based and variance-based SEM; and improves the conceptual knowledge by comparing the most important procedures and elements in the SEM study, using different theoretical criteria. Design/methodology/approach – The study thoroughly analyzes, reviews and presents two streams using common methodological background. The conceptual framework discusses the two streams by analysis of theory, measurement model specification, sample and goodness-of-fit. Findings – The paper identifies and discusses the use and misuse of covariance-based and variance-based SEM utilizing common topics such as: first, theory (theory background, relation to theory and research orientation); second, measurement model specification (type of latent construct, type of study, reliability measures, etc.); third, sample (sample size and data distribution assumption); and fourth, goodness-of-fit (measurement of the model fit and residual co/variance). Originality/value – The paper questions the usefulness of Cronbach's α research paradigm and discusses alternatives that are well established in social science, but not well known in the management research community. The author presents short research illustration in which analyzes the four recently published papers using common methodological background. The paper concludes with discussion of some open questions in management research practice that remain under-investigated and unutilized.

Study on the Integrated Method of SEM and DEMATEL

2014 ◽  
Vol 926-930 ◽  
pp. 3722-3727
Author(s):  
Wei Meng
Keyword(s):  
Decision Making ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Fitting ◽  
Assessment Model ◽  
Model Specification ◽  
Equation Modeling ◽  
Model Assessment ◽  
Integrated Method ◽  
Model Modification

This paper compares Structural Equation Modeling and Decision Making Trial and Evaluation Laboratory. Structural Equation Modeling and Decision Making Trial and Evaluation Laboratory are all methods to study factors’ structure problem. Some steps of the two methods can completely replace each other and complement each other. This paper puts forward an integrated method of Structural Equation Modeling and Decision Making Trial and Evaluation Laboratory that includes competing model specification, model fitting, model assessment, model modification and result explain.

scholarly journals umx: Twin and Path-based Structural Equation Modeling in R

2017 ◽  
Author(s):  
Timothy C Bates ◽  
Hermine H Maes ◽  
Michael C Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
The Social ◽  
Twin Models ◽  
Pathway Models

Structural equation modeling (SEM) is an important research tool, both for path-based model specification, common in the social sciences, and also matrix-based models in heavy use in behavior genetics. We developed umx to give more immediate access, concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification, and comparison of models, as well as both graphical and tabular output. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multi-group twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models including support for covariates, common- and independent-Pathway models, and Gene \(\times\) Environment interaction models. A tutorial site and question forum are also available.

scholarly journals umx: Twin and Path-based Structural Equation Modeling in R

2017 ◽  
Author(s):  
Timothy C Bates ◽  
Hermine H Maes ◽  
Michael C Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
The Social ◽  
Twin Models ◽  
Pathway Models

Structural equation modeling (SEM) is an important research tool, both for path-based model specification, common in the social sciences, and also matrix-based models in heavy use in behavior genetics. We developed umx to give more immediate access, concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification, and comparison of models, as well as both graphical and tabular output. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multi-group twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models including support for covariates, common- and independent-Pathway models, and Gene \(\times\) Environment interaction models. A tutorial site and question forum are also available.

scholarly journals Public Opinion in Policy Studies: Lessons from the Logic of Structural Equation Modeling

2019 ◽  
Author(s):  
Nate Breznau
Keyword(s):  
Public Opinion ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Context Effects ◽  
General Social Survey ◽  
Model Fitting ◽  
The United States ◽  
Model Specification ◽  
Equation Modeling ◽  
Policy Studies

Policy studies scholars regularly investigate linkages between public opinion and policy. The use of public opinion as a variable in empirical research poses special challenges. In this article I suggest that the logic and methods inherent in the art of structural equation modeling provide opportunities to overcome some of these challenges. I describe this type of logic as it pertains to measurement error, context effects and endogeneity. Using General Social Survey data for the United States and taking thermostatic feedback models as an example, I demonstrate why it is important for policy scholars to attend to (a) measurement modeling and cross-level isomorphism, (b) shocks that might bias survey response patterns and (c) endogeneity implied by the theoretically reciprocal nature of opinion and policy feedback. These examples come with discussions of why scholars should pay attention to model specification so that theory and empirics are in unison and how to perform model fitting and testing to better develop theories and models of policy processes.

A Brief Guide to Structural Equation Modeling

2006 ◽  
Vol 34 (5) ◽  
pp. 719-751 ◽  
Author(s):  
Rebecca Weston ◽  
Paul A. Gore
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Vocational Psychology ◽  
Model Specification ◽  
Equation Modeling ◽  
Fit Indices ◽  
Data Preparation ◽  
Alternative Models

To complement recent articles in this journal on structural equation modeling (SEM) practice and principles by Martens and by Quintana and Maxwell, respectively, the authors offer a consumer’s guide to SEM. Using an example derived from theory and research on vocational psychology, the authors outline six steps in SEM: model specification, identification, data preparation and screening, estimation, evaluation of fit, and modification. In addition, the authors summarize the debates surrounding some aspects of SEM (e.g., acceptable sample size, fit indices), with recommendations for application. They also discuss the need for considering and testing alternative models and present an example, with details on determining whether alternative models result in a significant improvement in fit to the observed data.

scholarly journals umx: Twin and Path-Based Structural Equation Modeling in R

2019 ◽  
Vol 22 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Timothy C. Bates ◽  
Hermine Maes ◽  
Michael C. Neale
Keyword(s):  
Structural Equation Modeling ◽  
Ordinal Data ◽  
Structural Equation ◽  
Behavior Genetics ◽  
Model Specification ◽  
Equation Modeling ◽  
Environment Interaction ◽  
Gene Environment ◽  
Twin Models ◽  
Pathway Models

AbstractStructural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

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