Nowadays, exploratory and confirmatory factor analyses are two important consecutive
steps in an overall analysis process. The overall analysis should start with an exploratory …

The following paper discusses exploratory factor analysis and gives an overview of the
statistical technique and how it is used in various research designs and applications. A basic …

Should one do a component analysis or a factor analysis? The choice is not obvious,
because the two broad classes of procedures serve a similar purpose, and share many …

Determining the number of factors is one of the most crucial decisions a researcher has to
face when conducting an exploratory factor analysis. As no common factor retention criterion …

This article examines effects of sample size and other design features on correspondence
between factors obtained from analysis of sample data and those present in the population …

Traditionally, two distinct approaches have been employed for exploratory factor analysis:
maximum likelihood factor analysis and principal component analysis. A third alternative …

Exploratory factor analysis (EFA) has emerged in the field of animal behavior as a useful tool
for determining and assessing latent behavioral constructs. Because the small sample size …

Exploratory factor analysis is used to identify and interpret correlational structures that may
be present in multivariate data. One effect of sampling error is a decrease in agreement …

A central problem in the application of exploratory factor analysis is deciding how many
factors to retain (m). Although this is inherently a model selection problem, a model selection …

Factor analysis is a method of modeling the covariation among a set of observed variables
as a function of one or more latent constructs. Here, we use the term construct to refer to an …