Member Training: Introduction to Structural Equation Modeling

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.

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About the Instructor

Manolo Romero Escobar is a seasoned Psychometrician and Assessment Scientist with a passion for delivering impactful solutions to clients. With expertise in assessment, evaluation, and measurement, Manolo excels in providing tailored consulting services to address the unique needs of his clients.

Throughout his career, Manolo has worked extensively as a research and statistical consultant, serving a diverse range of clients including health researchers, educational institutions, and government agencies. His specialties include factor analytical and latent-trait methods of measurement, as well as applications of linear mixed effects to nested, longitudinal, unbalanced data.

Manolo is also proficient in statistical programming languages such as R, Excel, SPSS, and Mplus, and has experience with Python and SQL. He is passionate about leveraging technology as an educational and training tool, and he continuously enhances his skills to stay at the forefront of his field.

With a focus on linear mixed effects modeling, latent variable modeling, and scale development, Manolo brings a wealth of knowledge and experience to every project he undertakes. He holds a B.A. and Licentiate degree in Psychology from Universidad del Valle de Guatemala and a M.A. in Psychology (Area: Developmental and Cognitive Processes) from York University.

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