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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 Exercises
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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

    Free

    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
  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
    Determining dimensionality
    50 xp
    Splitting the BFI dataset
    100 xp
    Calculating eigenvalues
    100 xp
    Creating a scree plot
    100 xp
    Interpreting the scree plot
    50 xp
    Understanding multidimensional data
    50 xp
    Conducting a multidimensional EFA
    100 xp
    Interpreting the results
    100 xp
    Investigating model fit
    50 xp
    Interpret absolute model fit statistics
    50 xp
    Selecting the best model
    100 xp
  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
    Setting up a CFA
    50 xp
    Creating CFA syntax from EFA results
    100 xp
    Creating CFA syntax from theory
    100 xp
    Understanding the sem() syntax
    50 xp
    Components of sem() syntax
    50 xp
    Run a CFA and interpret loadings
    100 xp
    Examine item loadings
    50 xp
    Investigating model fit
    50 xp
    Absolute fit statistics
    100 xp
    Relative fit statistics
    100 xp
  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
    EFA vs. CFA revisited
    50 xp
    Differences in estimated factor loadings
    100 xp
    Plotting differences in persons' factor scores
    100 xp
    Adding loadings to improve fit
    50 xp
    Add loadings to improve fit
    100 xp
    Compare original model to model with added loadings
    100 xp
    Evaluate added loadings with relative fit stats
    100 xp
    Improving fit by removing loadings
    50 xp
    Remove loadings to improve fit
    100 xp
    Compare original model to model with deleted loadings
    100 xp
    Evaluate deleted loadings with relative fit stats
    100 xp
    Wrap-Up Video
    50 xp

In the following tracks

Statistician with RUnsupervised Machine Learning with R

Datasets

Generic Conspiracist Beliefs Scale (GCBS) dataset

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Becca Robins

Prerequisites

Intermediate RFoundations of Inference in R
Jennifer Brussow HeadshotJennifer 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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