by Maike Rahn, PhD
In the previous blogs I wrote about the basics of running a factor analysis. Real-life factor analysis can become complicated. Here are some of the more common problems researchers encounter and some possible solutions:
- The factor loadings in your confirmatory factor analysis are only |0.5| or less.
Solution: lower the cut-offs of your factor loadings, provided that lower factor loadings are expected and accepted in your field.
- Your confirmatory factor analysis does not show the hypothesized number of factors.
Solution 1: you were not able to validate the factor structure in your sample; your analysis with this sample did not work out.
Solution 2: your factor analysis has just become exploratory. Something is going on with your sample that is different from the samples used in other studies. Find out what it is.
- A few key variables in your confirmatory factor analysis do not behave as expected and/or are correlated with the wrong factor.
Solution: the good news is that you found the hypothesized factors. The bad news is (more…)
by Maike Rahn, PhD
When are factor loadings not strong enough?
Once you run a factor analysis and think you have some usable results, it’s time to eliminate variables that are not “strong” enough. They are usually the ones with low factor loadings, although additional criteria should be considered before taking out a variable.
As a rule of thumb, your variable should have a rotated factor loading of at least |0.4| (meaning ≥ +.4 or ≤ –.4) onto one of the factors in order to be considered important. (more…)
by Maike Rahn, PhD
One of the hardest things to determine when conducting a factor analysis is how many factors to settle on. Statistical programs provide a number of criteria to help with the selection.
Eigenvalue > 1
Programs usually have a default cut-off for the number of generated factors, such as all factors with an eigenvalue of ≥1.
This is because a factor with an eigenvalue of 1 accounts for as much variance as a single variable, and the logic is that only factors that explain at least the same amount of variance as a single variable is worth keeping.
But often a cut-off of 1 results in more factors than the user bargained for or (more…)
by Maike Rahn, PhD
An important question that the consultants at The Analysis Factor are frequently asked is:
What is the difference between a confirmatory and an exploratory factor analysis?
A confirmatory factor analysis assumes that you enter the factor analysis with a firm idea about the number of factors you will encounter, and about which variables will most likely load onto each factor.
Your expectations are usually based on published findings of a factor analysis.
An example is a fatigue scale that has previously been validated. You would like to make sure that the variables in your sample load onto the factors the same way they did in the original research.
In other words, you have very clear expectations about what you will find in your own sample. This means that (more…)
by Maike Rahn, PhD
Rotations
An important feature of factor analysis is that the axes of the factors can be rotated within the multidimensional variable space. What does that mean?
Here is, in simple terms, what a factor analysis program does while determining the best fit between the variables and the latent factors: (more…)
Why use factor analysis?
Factor analysis is a useful tool for investigating variable relationships for complex concepts such as socioeconomic status, dietary patterns, or 
psychological scales.
It allows researchers to investigate concepts they cannot measure directly. It does this by using a large number of variables to esimate a few interpretable underlying factors.
What is a factor?
The key concept of factor analysis is that multiple observed variables have similar patterns of responses because they are all associated with a latent variable (i.e. not directly measured). (more…)
