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Structural Equation Modeling with lavaan
2018This book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers.
Gana, Kamel, Broc, Guillaume
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Frequentist Model Averaging in Structural Equation Modelling
Psychometrika, 2019Model selection from a set of candidate models plays an important role in many structural equation modelling applications. However, traditional model selection methods introduce extra randomness that is not accounted for by post-model selection inference.
Jin, Shaobo, Ankargren, Sebastian
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2018
A family of statistical techniques that has risen in popularity in conjunction with the availability of computing power and software is that of structural equation modelling (SEM). This chapter will provide an introduction to the basic principles of SEM analysis by constructing and fitting an example model.
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A family of statistical techniques that has risen in popularity in conjunction with the availability of computing power and software is that of structural equation modelling (SEM). This chapter will provide an introduction to the basic principles of SEM analysis by constructing and fitting an example model.
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An introduction to structural equation models
Journal of Clinical and Experimental Neuropsychology, 1988This paper provides an overview of structural equation models, and their potential for advancing neuropsychological theory and practice. Four topics are covered: (1) an overview of the various classes of models, and an introduction to the terminology and diagrams used to describe them, (2) an outline of the steps involved in applying structural ...
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Handling Uncertainty in Structural Equation Modeling
2016This paper attempts to propose an overview of a recent method named partial possibilistic regression path modeling (PPRPM), which is a particular structural equation model that combines the principles of path modeling with those of possibilistic regression to model the net of relations among variables.
Romano, Rosaria, PALUMBO, FRANCESCO
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An Introduction to Structural Equation Modeling
2015In this contribution the principles behind Structural Equation Modeling (SEM) are presented. SEM is used for assessing the quality of models that are proposed on the basis of theory and experience. This contribution has an introductory level.
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Power equivalence in structural equation modelling
British Journal of Mathematical and Statistical Psychology, 2010Implementing large‐scale empirical studies can be very expensive. Therefore, it is useful to optimize study designs without losing statistical power. In this paper, we show how study designs can be improved without changing statistical power by defining power equivalence , a relation between ...
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2015
AbstractThis chapter describes structural equation modeling (SEM), which represents a probabilistic modeling framework for studying causal hypotheses about systems. SEM relies on interconnected series of equations to represent networks as complex hypotheses.
James B. Grace +2 more
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AbstractThis chapter describes structural equation modeling (SEM), which represents a probabilistic modeling framework for studying causal hypotheses about systems. SEM relies on interconnected series of equations to represent networks as complex hypotheses.
James B. Grace +2 more
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2013
Structural equation modeling (SEM) refers to a flexible and rich analytic approach that can be used to address a variety of research questions through a combination of factor analysis, analysis of variance (ANOVA), and regression analysis.
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Structural equation modeling (SEM) refers to a flexible and rich analytic approach that can be used to address a variety of research questions through a combination of factor analysis, analysis of variance (ANOVA), and regression analysis.
openaire +4 more sources