There are two main types of factor analysis: exploratory and confirmatory. 
Exploratory factor analysis (EFA) is data driven, meaning the data informs the resulting factors. Confirmatory factor analysis (CFA) is model driven. Think of CFA as a process for testing whether the model you have works in new data.
You’ll learn about how CFA fits as a measurement model within the context of latent variable models. We’ll look at some examples from assessment development and other applications.
And finally, you’ll learn best practices and about model fit.
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.
About the Instructor
Manolo Romero Escobar is a seasoned statistical consultant and psychometrician with a passion for helping researchers.
Throughout his career, Manolo has worked extensively as a research and statistical consultant. He has served a diverse range of clients including health researchers, educational institutions, and government agencies. 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.
Manolo is also proficient in statistical programming languages such as R, 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.
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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