SEM

The Four Models You Meet in Structural Equation Modeling

By Manolo Romero Escobar

On a previous post (Why do I need to have knowledge of multiple regression to understand SEM?) we showed how a multiple regression model could be conceptualized using Structural Equation Model path diagrams.

That’s the simplest SEM you can create, but its real power lies in expanding on that regression model. Here I will discuss 4 ways to do that.

Path Analysis

More interesting research questions could be asked and answered using Path Analysis. Path Analysis is the application of structural equation modeling without latent variables. [Read more…] about The Four Models You Meet in Structural Equation Modeling

Five things you need to know before learning Structural Equation Modeling

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By Manolo Romero Escobar

If you already know the principles of general linear modeling (GLM) you are on the right path to understand Structural Equation Modeling (SEM).

As you could see from my previous post, SEM offers the flexibility of adding paths between predictors in a way that would take you several GLM models and still leave you with unanswered questions.

It also helps you use latent variables (as you will see in future posts).

GLM is just one of the pieces of the puzzle to fit SEM to your data. You also need to have an understanding of:
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Structural Equation Modeling: What is a Latent Variable?

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By Manolo Romero Escobar

What is a latent variable?

“The many, as we say, are seen but not known, and the ideas are known but not seen” (Plato, The Republic)

My favourite image to explain the relationship between latent and observed variables comes from the “Myth of the Cave” from Plato’s The Republic. In this myth a group of people are constrained to face a wall. The only things they see are shadows of objects that pass in front of a fire [Read more…] about Structural Equation Modeling: What is a Latent Variable?

Why do I need to have knowledge of multiple regression to understand SEM?

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by Manolo Romero Escobar

General Linear Model (GLM) is a tool used to understand and analyse linear relationships among variables. It is an umbrella term for many techniques that are taught in most statistics courses: ANOVA, multiple regression, etc.

In its simplest form it describes the relationship between two variables, “y” (dependent variable, outcome, and so on) and “x” (independent variable, predictor, etc). These variables could be both categorical (how many?), both continuous (how much?) or one of each.

Moreover, there can be more than one variable on each side of the relationship. One convention is to use capital letters to refer to multiple variables. Thus Y would mean multiple dependent variables and X would mean multiple independent variables. The most known equation that represents a GLM is: [Read more…] about Why do I need to have knowledge of multiple regression to understand SEM?