Structural Equation Modelling - A basic introduction.

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Structural Equation Modelling - A basic introduction.

Hi Folks, before I begin, I would like to give a overview about the content in this post.

This article gives a theoretical overview about the Structural Equation Modelling. This article is targeted and primarily helpful for beginners.

The main reason why I have been intended to write this article is to provide an easy understanding about the concept of SEM in a layman language.

I humbly admit that, I'm not going to deal with the entire concepts, principles and explanations related to SEM. However, I hope that this article at least provides you a better idea of the rudiments lying under the hood of SEM.

Let me give a quick introduction about myself(Sorry for the interruption). I'm a Software Engineer (Business Intelligence & Analytics). A person with ardent interest and curiosity in dealing with data. Data Science and Machine Learning enthusiast. Data Science and Machine Learning blogger. An R & Python(just started learning) programmer. As a layman, I can understand the difficulties in understanding and learning new concepts and technologies. So, I've started writing the articles in my blog to help such laymans like me. The articles publised in my blog are the result of my research on that particular published topic.

Let's continue,

In order to have a proper understanding of SEM, it is advisable to have a fair knowledge in Path Analysis and Factor Analysis.

To give a detailed explanation about the concepts and principles of Path analysis and Factor analysis requires a separate article for each. So, let me give a small introduction about Path analysis and Factor analysis before continuing with SEM.

Path Analysis: A form of multiple regression statistical analysis technique which is used to evaluate causal models by examining the relationships between the variables. The main difference between Path analysis and SEM is latent variables. SEM supports latent variables whereas path analysis
does not.

Factor Analysis: Factor analysis is applied as a data reduction or dimensionality reduction method.

The main applications of factor analytic techniques are
to reduce the number of variables and
  to detect structure in relationships between variables or to classify variables.

Let me continue with the SEM,

SEM is a multi-variate statistical analysis technique. Most of the sites you visit on the internet would refer SEM as a multivariate analysis technique. what exactly a multi-variate analysis technique is?

A multi-variate analytic technique gives a privilege to analyse more than one outcome variable ie., it gives an option to have more than one dependant variable in a single equation.

The following sentence might explain it more clearly.
SEM is a series of statistical methods that allows complex relationships between one or more independent variables and one or more dependent variables.

It's most prominent feature is the capability to deal with latent variables. SEM are often used to assess unobservable 'latent' constructs. It is used to analyze the structural relationship between measured variables and latent constructs. It estimates the multiple and interrelated dependence in a single analysis.

Many of us who would like to implement SEM in R or Python or any other programming languages might expect to implement it by importing the necessary packages and the functions available in those packages. Unlike the other univariate analytical techniques SEM cannot be implemented with a single function. SEM is a combination of multivariate analysis and factor analysis. So, in general SEM is a concept, a technique which comprises of several analytical methods to understand the relationships among the variables.

The purpose of SEM is to validate or reject one or more hypothesis about an existing relation between different variables. The ability of SEM to produce a meaningful identification of the correlations between factors is a key strength.

SEM is used to determine whether the exogenous(independent) variables are causally related to the endogenous(dependent) variables. SEM is largely a confirmatory, rather than exploratory technique. That is, SEM is used to determine whether a certain model is valid, rather than to find a suitable model.

Two main components of models are distinguished in SEM:
The Structural model - Showing potential causal dependencies between endogenous(dependent) and exogenous(Independent) variables.
The Measurement model - showing the relations between latent variables and their indicators.

Things to be aware about SEM:

SEM is not a Causal Model. This mis-interpretation of SEM as Causal model exists yet. Fit of a SEM is not a test of causality, although under specific
circumstances SEM can represent the causal relationships.

With this I would like to wind up this article. This article was drafted keeping in mind that at none of the situation it should become more complex for the readers to understand. In fact, I would like to remind it again that this article particularly focuses on the beginners. In my upcoming articles I will
give a detailed discussion about the SEM and other Data Science and Machine Learing related algorithms.

Please feel free to give your suggestions. I'm always reachable through the following links:

Email - kgfahath@gmail.com
LinkedIn - www.linkedin.com/in/fahath-kg

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