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• Structural_equation_modeling abstract "Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. This definition of SEM was articulated by the geneticist Sewall Wright (1921), the economist Trygve Haavelmo (1943) and the cognitive scientist Herbert A. Simon (1953), and formally defined by Judea Pearl (2000) using a calculus of counterfactuals.Structural equation models (SEM) allow both confirmatory and exploratory modeling, meaning they are suited to both theory testing and theory development. Confirmatory modeling usually starts out with a hypothesis that gets represented in a causal model. The concepts used in the model must then be operationalized to allow testing of the relationships between the concepts in the model. The model is tested against the obtained measurement data to determine how well the model fits the data. The causal assumptions embedded in the model often have falsifiable implications which can be tested against the data.With an initial theory SEM can be used inductively by specifying a corresponding model and using data to estimate the values of free parameters. Often the initial hypothesis requires adjustment in light of model evidence. When SEM is used purely for exploration, this is usually in the context of exploratory factor analysis as in psychometric design.[citation needed]Among the strengths of SEM is the ability to construct latent variables: variables that are not measured directly, but are estimated in the model from several measured variables, each of which is predicted to 'tap into' the latent variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which in theory allows the structural relations between latent variables to be accurately estimated. Factor analysis, path analysis and regression all represent special cases of SEM.In SEM, the qualitative causal assumptions are represented by the missing variables in each equation, as well as vanishing covariances among some error terms. These assumptions are testable in experimental studies and must be confirmed judgmentally in observational studies.".
• Structural_equation_modeling wikiPageExternalLink afni.nimh.nih.gov.
• Structural_equation_modeling wikiPageExternalLink PathAna.html.
• Structural_equation_modeling wikiPageExternalLink jw.html.
• Structural_equation_modeling wikiPageExternalLink ais.
• Structural_equation_modeling wikiPageExternalLink Handbook_of_Management_Scales.
• Structural_equation_modeling wikiPageExternalLink www.mvsoft.com.
• Structural_equation_modeling wikiPageExternalLink www.pls-sem.com.
• Structural_equation_modeling wikiPageExternalLink Book237345.
• Structural_equation_modeling wikiPageExternalLink structural-equation-modeling-reference-manual.
• Structural_equation_modeling wikiPageExternalLink www.statmodel.com.
• Structural_equation_modeling wikiPageExternalLink semrefs.htm.
• Structural_equation_modeling wikiPageExternalLink structur.htm.
• Structural_equation_modeling wikiPageExternalLink ~mkteer.
• Structural_equation_modeling wikiPageID "2007748".
• Structural_equation_modeling wikiPageRevisionID "604336589".
• Structural_equation_modeling date "April 2013".
• Structural_equation_modeling hasPhotoCollection Structural_equation_modeling.
• Structural_equation_modeling reason "link/ref for meaning of psychometric design?".
• Structural_equation_modeling subject Category:Econometrics.
• Structural_equation_modeling subject Category:Graphical_models.
• Structural_equation_modeling subject Category:Latent_variable_models.
• Structural_equation_modeling subject Category:Multivariate_statistics.
• Structural_equation_modeling subject Category:Psychometrics.
• Structural_equation_modeling subject Category:Regression_analysis.
• Structural_equation_modeling subject Category:Statistical_methods.
• Structural_equation_modeling subject Category:Structural_equation_models.
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• Structural_equation_modeling comment "Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. This definition of SEM was articulated by the geneticist Sewall Wright (1921), the economist Trygve Haavelmo (1943) and the cognitive scientist Herbert A.".
• Structural_equation_modeling label "Ecuaciones estructurales".
• Structural_equation_modeling label "Modelowanie równań strukturalnych".
• Structural_equation_modeling label "Structural equation modeling".
• Structural_equation_modeling label "Strukturgleichungsmodell".
• Structural_equation_modeling label "共分散構造分析".
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• Structural_equation_modeling wasDerivedFrom Structural_equation_modeling?oldid=604336589.
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