# Calculating eigenvalues

To empirically determine the dimensionality of your data, a common strategy is to examine the eigenvalues. Eigenvalues are numeric representations of the amount of variance explained by each factor or component. Eigenvalues are calculated from a correlation matrix, so you'll need to use `cor()`

to calculate and store the dataset's correlation matrix before calculating eigenvalues. You'll need to specify that you want to use pairwise complete observations. The default is to use everything, but if your dataset has any missing values, this will leave you with a matrix full of NAs.

You'll do these calculations on the `bfi_EFA`

dataset you just created - remember, you're saving the data in `bfi_CFA`

for your confirmatory analysis!

This is a part of the course

## “Factor Analysis in R”

### Exercise instructions

- Use
`cor()`

to calculate the correlation matrix for your EFA dataset. Set the value of the`use`

argument to use pairwise-complete observations. - Next, use that correlation matrix with the
`eigen()`

function to get eigenvalues. - The eigenvalues are stored in the
`values`

element of the`eigenvals`

list object. Take a look!

### Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

```
# Calculate the correlation matrix first
bfi_EFA_cor <- ___(bfi_EFA, use = ___)
# Then use that correlation matrix to calculate eigenvalues
eigenvals <- ___(bfi_EFA_cor)
# Look at the eigenvalues returned
___$___
```

This exercise is part of the course

## Factor Analysis in R

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

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

Exercise 1: Determining dimensionalityExercise 2: Splitting the BFI datasetExercise 3: Calculating eigenvaluesExercise 4: Creating a scree plotExercise 5: Interpreting the scree plotExercise 6: Understanding multidimensional dataExercise 7: Conducting a multidimensional EFAExercise 8: Interpreting the resultsExercise 9: Investigating model fitExercise 10: Interpret absolute model fit statisticsExercise 11: Selecting the best model### What is DataCamp?

Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.