How Factorable is our Dataset?

As mentioned in the video, before reducing dimensions with EFA, we first need to make sure that our dataset is factorable. In other words, the first step in performing EFA is to check whether it is even worth doing it. This dilemma is captured by the following question: Is there sufficient correlation among the observed variables of our dataset to allow for dimensionality reduction in the first place?

hsq_polychoric, calculated with the hetcor() function of the polycor package, is the correlation matrix of the Humor Styles Questionnaire [HSQ] dataset that you will be working throughout this and part of the next chapter.

In this exercise, your mission is to decide whether HSQ is factorable enough to allow an EFA.

Apply the Bartlett sphericity test on hsq_polychoric. For an EFA to be considered suitable, the Bartlett sphericity test result must be less than 0.05 to be deemed statistically significant.
The second test we will use is the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. Recall that the closer the value to 1 is the more effectively and reliably the reduction will be. Notice the indices for each variable in the output.

Exercise

How Factorable is our Dataset?

Instructions