Contents
What does exploratory factor analysis measure?
In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables.
How many participants are needed for exploratory factor analysis?
Although there are rules of thumb such as 5 or 10 participants per item, these are simply rules of thumb. Sometimes smaller EFA studies with ns of 150 (depending on the type of FA and number of items) can be useful.
What is the goal of exploratory factor analysis?
In exploratory factor analysis (EFA, the focus of this resource page), each observed variable is potentially a measure of every factor, and the goal is to determine relationships (between observed variables and factors) are strongest.
What are the two types of factor analysis?
There are two main types of factor analysis: exploratory and confirmatory. In exploratory factor analysis (EFA, the focus of this resource page), each observed variable is potentially a measure of every factor, and the goal is to determine relationships (between observed variables and factors) are strongest.
How is factor analysis used in the real world?
Factor analysis is a 100-year-old family of techniques used to identify the structure/dimensionality of observed data and reveal the underlying constructs that give rise to observed phenomena. The techniques identify and examine clusters of inter-correlated variables; these clusters are called “factors” or “latent variables” (see Figure 1).
How are scree plots used in factor analysis?
Scree plots (Figure 5 below) are common output in factor analysis software, and are line graphs of eigenvalues. They depict the amount of variance explained by each factor, and the “cut off” is the number of factors right before the “bend” in the scree plot, e.g., around 2 or 3 factors in Figure 5.