# Latent Growth Curve Model

### Member Training: Introduction to Structural Equation Modeling

June 1st, 2024 by

Structural Equation Modeling (SEM) is a popular method to test hypothetical relationships between constructs in the social sciences. These constructs may be unobserved (a.k.a., “latent”) or observed (a.k.a., “manifest”).

In this training, you will learn the different types of SEM: confirmatory factor analysis, path analysis for manifest and latent variables, and latent growth modeling (i.e., the application of SEM on longitudinal data).

We’ll discuss the different terminology, the commonly used symbols, and the different ways a model can be specified, as well as how to present results and evaluate the fit of the models.

This training will be at a very basic conceptual level; however, it is assumed that participants have an understanding of multiple regression, interpretation of statistical tests, and methods of data screening.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

### 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…)

### Member Training: A Guide to Latent Variable Models

July 1st, 2020 by

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.

### One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model

February 23rd, 2020 by

Based on questions I’ve been asked by clients, most analysts prefer using the factor analysis procedures in their general statistical software to run a confirmatory factor analysis.

While this can work in some situations, you’re losing out on some key information you’d get from a structural equation model. This article highlights one of these.

### First Steps in Structural Equation Modeling: Confirmatory Factor Analysis

February 7th, 2020 by

Confirmatory factor analysis (CFA) is the fundamental first step in running most types of SEM models. You want to do this first to verify the measurement quality of any and all latent constructs you’re using in your structural equation model.

### Member Training: Reporting Structural Equation Modeling Results

October 1st, 2019 by

The last, and sometimes hardest, step for running any statistical model is writing up results.

As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.

Any good statistical report includes enough information that someone else could replicate your results with your data.