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Factor Analysis in R

via Datacamp


Explore latent variables, such as personality using exploratory and confirmatory factor analyses.

The world is full of unobservable variables that can't be directly measured. You might be interested in a construct such as math ability, personality traits, or workplace climate. When investigating constructs like these, it's critically important to have a model that matches your theories and data. This course will help you understand dimensionality and show you how to conduct exploratory and confirmatory factor analyses. With these statistical techniques in your toolkit, you'll be able to develop, refine, and share your measures. These analyses are foundational for diverse fields including psychology, education, political science, economics, and linguistics.


Evaluating your measure with factor analysis
-In Chapter 1, you will learn how to conduct an EFA to examine the statistical properties of a measure designed around one construct.

Multidimensional EFA
-This chapter will show you how to extend the single-factor EFA you learned in Chapter 1 to multidimensional data.

Confirmatory Factor Analysis
-This chapter will cover conducting CFAs with the sem package. Both theory-driven and EFA-driven CFA structures will be covered.

Refining your measure and/or model
-This chapter will reinforce the difference between EFAs and CFAs and offer suggestions for improving your model and/or measure.

Taught by

Jennifer Brussow

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