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 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.

Should I suggest this YouTube clip? The impact of UGC source credibility on eWOM and purchase intention

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mazzini Muda ◽  
Muhammad Iskandar Hamzah
Keyword(s):  
Social Media ◽  
Structural Equation Modeling ◽  
Purchase Intention ◽  
Structural Equation ◽  
Word Of Mouth ◽  
Source Credibility ◽  
Equation Modeling ◽  
Content Type ◽  
The Social ◽  
The Impact

PurposeIn spite of the increasing organic and interactive marketing activities over social media, a general understanding of the source credibility of voluntary user-generated content (UGC) is still limited. In line with the social identity theory, this paper examines the effects of consumers' perceived source credibility of UGC in YouTube videos on their attitudes and behavioral intentions. Additionally, source homophily theory is included to predict the antecedent of source credibility.Design/methodology/approachThree hundred and seventy two Generation Y respondents were interviewed using snowball sampling. Data were analyzed with component-based structural equation modeling technique of partial least squares-structural equation modeling (PLS-SEM).FindingsFindings confirmed that perceived source credibility indirectly affects purchase intention (PI) and electronic word-of-mouth via attitude toward UGC. Besides, perceived source credibility mediates the effect of perceived source homophily on attitude toward UGC.Practical implicationsSince today's consumers have begun to trust and rely more on UGC than company-generated content on social media when making purchase decisions, companies may reconsider democratizing certain aspects of their branding strategies. Firms may fine-tune their marketing communication budgets – not only just by sponsoring public figures and celebrities but also by nurturing coproductive engagements with independent content creators who are ordinary consumers. Endowed with their imposing credibility, these micro-influencers and prosumers have high potentials to be uplifted to brand ambassadors.Originality/valueWhile consumers' purchase outcome can be measured easily using metrics and analytics, the roles of source homophily in stages leading up to the purchase is still elusive. Drawing on the rich theoretical basis of source homophily may help researchers to understand not only how credibility and attitude are related to PI but also how this nexus generates positive word of mouth among UGC followers within the social media circles.

Structural equation modeling of residents' attitudes and tourism development: a SET based - study of the Kashmir region in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aasif Ali Bhat ◽  
Kakali Majumdar
Keyword(s):  
Structural Equation Modeling ◽  
Social Exchange ◽  
Structural Equation ◽  
Social Exchange Theory ◽  
Tourism Development ◽  
Exchange Theory ◽  
Primary Data ◽  
Equation Modeling ◽  
Content Type ◽  
The Social

PurposeThe present study tries to develop a model that assesses the factors that determine support for tourism development by residents of the Kashmir region.Design/methodology/approachPrimary data have been collected (n = 650) from the residents of the top five tourist destinations through a pre-tested questionnaire by a multistage convenient sampling method. A model has been drafted and tested through the technique of structural equation modeling by applying the social exchange theory as a theoretical framework.FindingsThe results revealed that residents who perceived more benefits were more expected to support tourism development, and residents who perceive more costs were less expected to support tourism development, thus supporting the social exchange theory.Originality/valueThe results of this study are extremely useful for the local government and tourism institutions in the future planning of tourism development and also fill the vast gap in the tourism literature with a theoretical base.

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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