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Factor Analysis in R

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

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4 Hours13 Videos45 Exercises8,313 Learners3600 XPStatistician TrackUnsupervised Machine Learning Track

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Course Description

Discover Factor Analysis in R

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.

Learn to Use Exploratory Factor Analysis and Confirmatory Factor Analysis

You’ll start by getting to grips with exploratory factor analysis (EFA), learning how to view and visualize factor loadings, interpret factor scores, and view and test correlations.

Once you’re familiar with single-factor EFA, you’ll move on to multidimensional data, looking at calculating eigenvalues, creating screen plots, and more. Next, you’ll discover confirmatory factor analysis (CFAs), learning how to create syntax from EFA results and theory.

The final chapter looks at EFAs vs CFAs, giving examples of both. You’ll also learn how to improve your model and measure when using them.

Develop, Refine, and Share Your Measures

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
    Introduction to Exploratory Factor Analysis (EFA)
    50 xp
    Starting out with a unidimensional EFA
    100 xp
    Viewing and visualizing the factor loadings
    100 xp
    Interpreting individuals' factor scores
    100 xp
    Overview of the measure development process
    50 xp
    Descriptive statistics of your dataset
    100 xp
    Splitting your dataset
    100 xp
    Comparing the halves of your dataset
    100 xp
    Measure features: correlations and reliability
    50 xp
    Viewing and testing correlations
    100 xp
    Internal reliability
    100 xp
    When to use EFA
    50 xp

In the following tracks

StatisticianUnsupervised Machine Learning


Chester Ismay
Becca 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
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