Exploratory Factor Analysis in R

PromptCloud
Aug 22, 2017 · 2 min read

Exploratory Factor Analysis (EFA) is a statistical technique that is used to identify the latent relational structure among a set of variables and narrow down to smaller number of variables. This essentially means that the variance of large number of variables can be described by few summary variables, i.e., factors. Here is an overview of exploratory factor analysis:

As the name suggests, EFA is exploratory in nature — we don’t really know the latent variables and the steps are repeated until we arrive at lower number of factors. In this tutorial we’ll look at EFA using R. Now, let’s first get the basic idea of the dataset.

The Data

This dataset contains 90 responses for 14 different variables that customers consider while purchasing car. The survey questions were framed using 5-point likert scale with 1 being very low and 5 being very high. The variables were the following:

  • Price
  • Safety
  • Exterior looks
  • Space and comfort
  • Technology
  • After sales service
  • Resale value
  • Fuel type
  • Fuel efficiency
  • Color
  • Maintenance
  • Test drive
  • Product reviews
  • Testimonials

Click here to download the coded dataset.

Importing Data

Now we’ll read the dataset present in CSV format into R and store it as a variable.

1

data <- read.csv(file.choose(),header=TRUE)

It’ll open a window to choose the CSV file and header option will make sure that the the first row of the file is considered as the header. Enter the following to see the first several rows of the data frame and confirm that the data has been stored correctly. Continue reading…

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