# Thesis using structural equation modeling Here un-standardized is used since it takes into account standard deviation of two variables. Due to association of covariance with SEM facilitates in:. Covariance matrix also called variance-covariance matrix or dispersion matrix, is a matrix that contains the covariance of the element x and element y at the xth and y th position of a random vector in the matrix wherein a random vector is a random variable that has many dimensions.

A covariance matrix is created after estimating the parameters on the model. The matrix is then compared with the covariance matrix of the data collected.

## Dissertation/Thesis Abstract

Model is derived from a theory based on a concept. A fit theory creates a fit model. Thus it is necessary to meet but insufficient condition for the theory has to be valid. The condition talks about the theory to be able to reproduce the correlation values that were actually observed in the data. Thus the covariance matrix of the theory or model should be equal or similar to the one obtained from the data. Regression analysis finds the impact of independent variables on the dependent variable.

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But SEM is better than regression because of following reasons:. The biggest advantage of SEM over other model-driven statistical techniques is that it is capable of simultaneously testing the measurement relationship and structural relationship between the set of variables taken into consideration. The reason for the same is the fact that it is a combination of regression analysis and factor analysis. It involved the relation of covariance with SEM and the concept of theory fit. The article also compared and contrasted it with regression analysis and presented the advantages, uses, and types of SEM.

An Introduction to SEM Using SmartPLS

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### Theory fit for model fit

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## PhD Dissertation – ResearchHUB

Explaining as much variance as possible by the specified model in SEM. Structural equation model SEM versus Regression analysis Regression analysis finds the impact of independent variables on the dependent variable. But SEM is better than regression because of following reasons: SEM allows multiple dependent variables while regression allows only one dependent variable. This is because it runs a series of multiple regression analysis simultaneously.

## Theory and application of structural equation modeling in spatial data analysis

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