There are two main types of factor analysis: exploratory and confirmatory. 
Stage 3
Member Training: Confirmatory Factor Analysis
March 3rd, 2025 by TAF Support
Member Training: Exploratory Factor Analysis
February 1st, 2025 by Karen Grace-Martin
Many variables we want to measure just can’t be directly measured with a single variable. Instead you have to combine a set of variables into a single index.
But how do you determine which variables to combine and how best to combine them?
Exploratory Factor Analysis.
EFA is a method for finding a measurement for one or more unmeasurable (latent) variables from a set of related observed variables. It is especially useful for scale construction.
In this webinar, you will learn through three examples an overview of EFA, including:
- The five steps to conducting an EFA
- Key concepts like rotation
- Factor scores
- The importance of interpretability
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
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
Just head over and sign up for Statistically Speaking. You'll get access to this training webinar, 130+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians.
Member Training: Introduction to Binary Logistic Regression
December 3rd, 2024 by TAF Support
Binary logistic regression is one of the most useful regression models. It allows you to predict, classify, or understand explanatory relationships between a set of predictors and a binary outcome.
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Member Training: Introduction to Item Response Theory and Rasch Models
November 1st, 2024 by TAF Support
How do you know if the items of a test are hard or easy; fair or biased; accurate at measuring ability or not?
Item Response Theory (IRT).
In this training, you will see, with real life examples, how IRT answers these questions to assess a test.
Member Training: Types of Longitudinal, Repeated Measures, and Time Series
October 2nd, 2024 by TAF Support
How do you know when to use a time series and when to use a linear mixed model for longitudinal data?
What’s the difference between repeated measures data and longitudinal?
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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.