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Factor analysis is your friend: Quit whining and learn it

Too often, when I look at the surveys some people design, I have the same thought as when I see my granddaughter with a lollipop bigger than her head –

Just what exactly do you think that you are going to DO with that?

Eva with huge lollipop

The problem is that both may have metaphorically (or, in Eva’s case, literally) have bitten off more than they can chew.

Okay, great, you asked 72 questions on your survey, received 1,873 surveys back and most people answered most of them. You could try throwing everything into data mining software with your 72 items and hope for the best but that presumes a) you have some data mining software handy and b) an understanding of test sets and validation. I’m going with the more likely scenario that the answer to either a) or b) is

Um – no.

Imagine yourself in this scenario – someone, maybe you, has collected survey data at great expense. Maybe you paid subjects to answer questions about themselves, gave students credit to participate in a study, and now you have dozens, perhaps hundreds, of variables on each person. How on earth do you analyze these data? You could just go through and start putting questions together to form subscales, but that is pretty arbitrary. Enter factor analysis to help you make sense of your data.

Factor analysis is extremely useful. Conceptually, it is relatively easy to understand – mathematically, um, not so much so.

You take a large number of questions and find what few, underlying traits they represent, such as supervision, collaborative decision making and ambition.

So, for example, the Weschler Intelligence Scale has many, many items. These can be combined into subscales such as information, comprehension, object  assembly and coding. The subscales can be further aggregated into two scores – a Verbal IQ and a Performance IQ.

This is based on the belief expressed by Wechsler who said that some people were good at reasoning with words and other people are good at reasoning with things but that both were types of intelligence. Writing a paper displays your intelligence, but so does putting together a computer or designing a part for it. So, said Wechsler, let’s have a bunch of items that measure those two factors, add up the scores on those items and get our two types of IQ.

Ever since I watched this TED talk by Conrad Wolfram on how math does not equal computation (and he is, of course, right), I’ve been thinking about how to apply it to the work we do here at The Julia Group.

Factor analysis is one example. The math behind it can be fairly daunting, but the actual concept is quite simple, and there are tools like SPSS and SAS Enterprise Guide that now eliminated the need to learn programming.

Still …. how do you know the number of factors? How do you decide which survey item goes with which factor? Why would you rotate and which rotation would you use?

Stay tuned and … later this week I will explain the answer to those questions and more. I know you can hardly wait.

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