# Structural Equation Modeling

### Member Training: Moderated Mediation, Not Mediated Moderation

February 28th, 2023 by

Moderated mediation, also known as Conditional Process Modeling, is great tool for understanding one type of complex relationship among variables. ### The Four Models You Meet in Structural Equation Modeling

August 8th, 2022 by 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. (more…)

### Exogenous and Endogenous Variables in Structural Equation Modeling

July 22nd, 2022 by 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

March 4th, 2021 by 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

October 23rd, 2020 by 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

July 22nd, 2020 by

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.