Factor Analysis in R

Explore latent variables, such as personality using exploratory and confirmatory factor analyses.
Start Course for Free
4 Hours13 Videos45 Exercises5,828 Learners
3600 XP

Create Your Free Account

By continuing you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.

Loved by learners at thousands of companies

Course Description

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.

  1. 1

    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.
    Play Chapter Now
  2. 2

    Multidimensional EFA

    This chapter will show you how to extend the single-factor EFA you learned in Chapter 1 to multidimensional data.
    Play Chapter Now
  3. 3

    Confirmatory Factor Analysis

    This chapter will cover conducting CFAs with the sem package. Both theory-driven and EFA-driven CFA structures will be covered.
    Play Chapter Now
  4. 4

    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.
    Play Chapter Now
In the following tracks
Unsupervised Machine Learning
Chester IsmayBecca Robins
Jennifer Brussow Headshot

Jennifer Brussow

Psychometrician at Ascend Learning
I’m a researcher, programmer, and statistician with a Ph.D. in Research, Evaluation, Measurement, and Statistics from the University of Kansas. I spend most of my time analyzing data, thinking about statistical models, and learning new tricks in R. I also make programming-related cross stitch designs and sell them on my Etsy store, commandlineXstitch. For more about me, visit my website
See More

What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA