In many domains (marketing, social sciences, etc.) the variables of interest are often not directly observable. Complex causal relationships between such latent variables may be modeled by means of Structural Equation Models (SEM). Two different methodologies exist for the estimation of such models: SEM-ML (Maximum Likelihood Approach to Structural Equation Models), also known as LISREL (Linear...

Submit Manuscript | http://medcraveonline.com Abbreviations: NICRH: National Institute of Cancer Research and Hospital; WHO: World Health Organization; BSMMU: Bangabandhu Sheikh Mujib Medical University; PLSpath model: Partial least squares path model; SEM: Structural Equation Modeling; BMI: Body Mass Index; PLSPM: Results of Partial Least Squares Path Models; IARC: International Agency for Res...

In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree structures that separate a data set recursively into s...

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to...

Uncovering unobserved heterogeneity is a requirement to obtain valid results when using the structural equation modeling (SEM) method with empirical data. Conventional segmentation methods usually fail in SEM since they account for the observations but not the latent variables and their relationships in the structural model. This research introduces a new segmentation approach to variance-based...

in this study, the effect of environmental concern, attitudes and behaviors of the university students on the environmentally conscious purchasing behavior was investigated with the help of structural equation model (sem). sem is an effective data analysis tool that expresses the complicated causative relations between the latent variables. in this study, we collected the data from 400 univers...

Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-C...

Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions [1]– [3]. SEM allows for both confirmatory and exploratory modeling. In exploratory modeling one starts with the specification of a hypothesis, which is tested against measurements by measuring how well the model fi...

The correlation analysis of the independence between the behavioral influencing factors has been performed in order to find the non-independent factors. Among the Human Factors Analysis and Classification System (HFACS), the impact of the high-level factors on the bottom level is determined. This study uses empirical method, namely Structural Equation Modeling (SEM) to build the relationship mo...

R is free, open-source, cooperatively developed software that implements the S statistical programming language and computing environment. The current capabilities of R are extensive, and it is in wide use, especially among statisticians. The sem package provides basic structural equation modeling facilities in R, including the ability to fit structural equations in observed variable models by ...