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.

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 Gene-Environment Interplay in Twin Models

2013 ◽  
Vol 21 (3) ◽  
pp. 368-389 ◽  
Author(s):  
Brad Verhulst ◽  
Peter K. Hatemi
Keyword(s):  
Genetic Similarity ◽  
Behavior Genetics ◽  
Empirical Studies ◽  
Environment Interaction ◽  
Twin Design ◽  
Gene Environment Interaction ◽  
Twin Model ◽  
Gene Environment ◽  
Twin Models ◽  
Classical Twin Design

In this article, we respond to Shultziner's critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism's mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise.

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.

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