This little column is to remind those people like me of one of those things that get left by the edge: factor analysis. It's more than just knowing mean and variance,so in my own little understanding I’ll try explaining why it’s fun and interesting. So if we look it at what factor analysis attempts to do is that it tries to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables. Factor analysis is often used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of manifest variables.
Ok... before I lose every last one of you from reading this, here are few examples of why factor analysis is fun.
First example is on something that I stumbled upon while trying to learn more about this topic it’s an online dating site called Eharmony.com. They follow a system to match you with someone you're most compatible with, and how do they do it? Factor analysis, of course. They give potential love birds a survey of a few hundred questions (oh, the tedium!) and then map the test-taker in a few artificial dimensions. They find people who are close in the artificial space, and have them go out on dates and thus the so called romance blossoms. To the guys reading this it would be rude of me to encourage to use factor analysis without mentioning that Eharmony has patented their matchmaking algorithm.(Hmph...tough luck creating one..:S)
Oh, and the other thing that caught my eye was a paper about colour perception: for sighted people, factor analysis neatly puts the perception of words such as `red', `blue', and `yellow' in two dimensions, in a circle---a colour wheel. Factor analysis of responses to the same words by the blind fall on one dimension, basically ranging from bright to dark. And thus, factor analysis shows us what the blind see.
There are two ways you could go about it, I guess: the first is to say, `I have no idea how the data was generated, but darn it, I want an image. The other is to say `I really think there are some latent variables driving the variables I've observed', and then factor analysis may again save the day, by showing you the best linear combination of existing variables to suggest what those hidden variables may be. Both of these sorts of behaviour are exactly what statistics is really about, and think they're a great thing to try on any given data set.
This all comes up because I was thinking (yea yea...me finally thinking) and came up with on how college kids or I rather call them adults smoke (I guess you can wonder why i got really interested) and having some information about these people over the years helped me think and analyze my data hence I wanted to amuse myself with it more. So I'm not assuming that there are important latent variables underlying what data I have. The factor analysis did a very neat job of pulling out what I reasonably believe are the most important underlying characteristics, thus saving the day. Thus using factor analysis on any of your favourite data set can produce good results and from a good factor analysis really can teach the world stuff.
Author :- Lincy ThomasGroup :- Operation 3