Course
Factor Analysis in R
AdvancedSkill Level
Updated 10/2020
RProbability & Statistics4 hr13 videos45 Exercises3,600 XP12,126Statement of Accomplishment
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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."
Prerequisites
Intermediate RFoundations of Inference in R1
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.
2
Multidimensional EFA
This chapter will show you how to extend the single-factor EFA you learned in Chapter 1 to multidimensional data.
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.
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.
Factor Analysis in R
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FAQs
What is factor analysis?
Researchers use factor analysis as a data reduction technique, allowing them to investigate concepts that aren’t easy to measure directly.
What is the difference between factor analysis and PCA?
While factor analysis seeks latent factors in observed data, principal component analysis (PCA) seeks to identify variables that are composites of observed variables.
Why is factor analysis done?
Factor analysis can help you to simplify your data by reducing the number of variables in regression models. With factor analysis, you can reduce a wide range of individual items into fewer dimensions.
What are latent variables?
A latent variable is a variable that cannot be observed. They are detected by the effect they have on observable variables.
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