Exploratory Factor Analysis – Exercises

June 14, 2017
By

(This article was first published on R-exercises, and kindly contributed to R-bloggers)

This set of exercises is about exploratory factor analysis. We shall use some basic features of `psych` package. For quick introduction to exploratory factor analysis and `psych` package, we recommend this short “how to” guide.

Answers to the exercises are available here.

If you have different solution, feel free to post it.

Exercise 1

Load the data, install the packages `psych` and `GPArotation` which we will use in the following exercises, and load it. Describe the data.

Exercise 2

Using the parallel analysis, determine the number of factors.

Exercise 3

Determine the number of factors using Very Simple Structure method.

Exercise 4

Based on normality test, is the Maximum Likelihood factoring method proper, or is OLS/Minres better? (Tip: Maximum Likelihood method requires normal distribution.)

Exercise 5

Using oblimin rotation, 5 factors and factoring method from the previous exercise, find the factor solution. Print loadings table with cut off at 0.3.

Exercise 6

Exercise 7

Plot structure diagram.

Exercise 8

Find the higher-order factor model with five factors plus general factor.

Exercise 9

Find the bifactor solution.

Exercise 10

Reduce the number of dimensions using hierarchical clustering analysis.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...