Jennie Pearson

When I first started out after undergrad I was intimidated by many things (more than I care to admit). A number of those things involved statistics. Especially those with fancy names like Multivariate Data Analysis, Factor Analysis, Multiple Linear Regression, Cluster Analysis, Principal Components, Time-Series, etc. But once I got to grad school and started learning about all of these, I realized they were all so much easier than I had thought. So easy in fact, that I feel silly for ever being intimidated by them. So I thought I would share with you what some of these things are and show you how simple they are. We will start with Factor Analysis.

What is Factor Analysis:

Many statistical techniques are used to examine relationships between a dependent variable and independent variable(s). In that regard, Factor Analysis is different. Factor Analysis attempts to detect patterns or relationships among a set of defined variables. Basically, all Factor Analysis is, is a grouping of correlated variables into factors, or unobserved (latent) constructs. The assumption is that the latent constructs explain the correlation among the observed (not latent) variables. You can use it to takes long list of items and group them together. Thus, not surprisingly, it is commonly known as a data reduction technique.

Unlike regression techniques, you can’t use FA to make predictions about anything. For example, you could never use FA to say customers with XYZ characteristics are more likely to prefer Product A over Product B. But what you can do is attempt to define underlying constructs. This can be useful when presenting results of a survey to a client who wants to know what qualities are important to their customers or identify attributes of a product that are important, or determine the characteristics of a company’s highest performing employees.

For example, in a market research survey, you might ask respondents to rate Company X on a list of attributes. Attributes can include things like uniqueness, attractiveness, humorous, enjoyment, ease of shopping, customer service, proximity of stores, cleanliness of stores, corporate responsibility, expensive, cheap, convenient hours, luxury, whatever. Often times, attributes will be correlated, like cleanliness of stores, ease of shopping, convenient hours and customer service. These attributes might be measuring the similar ideas or constructs. In this example, these three variables may be measuring attitudes toward in-store shopping.

I won’t go deeply into the difference between Common Factor Analysis (CFA) and Principal Component Analysis (PCA). Just know that they are two similar but distinct types of factor analysis. Both are data reduction techniques but they make very different assumptions about the variance. More information can be found here:
http://www.ats.ucla.edu/stat/sas/library/factor_ut.htm

When to use Factor Analysis:

Factor Analysis can be used when your survey contains a lot of correlated variables that may be measuring similar underlying constructs. So instead of reporting scores for each individual attribute, you can distill all the attributes into multiple groupings, or factors. A large sample size is also important.

And that’s it! Not too intimidating after all, was it?