Structural Equation Modeling

The Four Models You Meet in Structural Equation Modeling

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 four types of structural equation models.

Path Analysis

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

Exogenous and Endogenous Variables in Structural Equation Modeling

by Karen Grace-Martin Leave a Comment

In most regression models, there is one response variable and one or more predictors. From the model’s point of view, it doesn’t matter if those predictors are there to predict, to moderate, to explain, or to control. All that matters is that they’re all Xs, on the right side of the equation.

Member Training: Goodness of Fit Statistics

by TAF Support

What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?

Measurement Invariance and Multiple Group Analysis

by Jeff Meyer 2 Comments

Creating a quality scale for a latent construct (a variable that cannot be directly measured with one variable) takes many steps. Structural Equation Modeling is set up well for this task.

One important step in creating scales is making sure the scale measures the latent construct equally well and the same way for different groups of individuals.

Why Adding Values on a Scale Can Lead to Measurement Error

by Jeff Meyer 4 Comments

Whenever you use a multi-item scale to measure a construct, a key step is to create a score for each subject in the data set.

This score is an estimate of the value of the latent construct (factor) the scale is measuring for each subject. In fact, calculating this score is the final step of running a Confirmatory Factor Analysis.

Member Training: A Guide to Latent Variable Models

by Jeff Meyer

An extremely useful area of statistics is a set of models that use latent variables: variables whole values we can’t measure directly, but instead have to infer from others. These latent variables can be unknown groups, unknown numerical values, or unknown patterns in trajectories.