When analyzing longitudinal data, do you use regression or structural equation based approaches? There are many types of longitudinal data and different approaches to analyzing them. Two popular approaches are a regression based approach and a structural equation modeling based approach.
Membership Webinars
Member Training: Analyzing Longitudinal Data: Comparing Regression and Structural Equation Modeling Approaches
July 2nd, 2024 by TAF Support
Member Training: Introduction to Structural Equation Modeling
June 1st, 2024 by guest contributer
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.
Member Training: Linear Regression in SPSS (Tutorial)
March 29th, 2024 by Kat Caldwell
Regression is one of the most common analyses in statistics. Most of us learn it in grad school, and we learned it in a specific software. Maybe SPSS, maybe another software package. The thing is, depending on your training and when you did it, there is SO MUCH to know about doing a regression analysis in SPSS.
Member Training: Coarsened Exact Matching, an Alternative to Propensity Score Matching
February 29th, 2024 by TAF Support
The objective for quasi-experimental designs is to establish cause and effect relationships between the dependent and independent variables. However, they have one big challenge in achieving this objective: lack of an established control group.
Member Training: Effective File and Process Management in Statistical Projects
January 31st, 2024 by TAF Support
Member Training: Interactions in Poisson and Logistic Regression – Part 2
December 1st, 2023 by Kim Love
Interactions in statistical models are never especially easy to interpret. Throw in non-normal outcome variables and non-linear prediction functions and they become even more difficult to understand. (more…)