Finding Correlations

#1
I'm trying to find some clusters/ segments or correlations between data based on survey results. I've attached a simplified version of the data. Can anyone help me make sense of it? I would be most obliged. Thanks.
 

Attachments

#3
Thank you for replying to my question, which clearly is not stated very clearly :S After watching a number of youtube videos, I learned that the matrix shows a negative correlation between design abilities, food and menu creation, and tech savviness. What does that mean exactly? What else can I discern from this? Is there a way I can segment this population based on this information?
 
#4
Hi Ewa,

First, not all the correlation values in the matrix are necessarily significant ...need to be proved. (but the sample size is large)
For example, -0.004 may not be significant. (I didn't test)

The method is usually the opposite, first, you think what you want to achieve and then you use the statistics.

What do you look for?
 
#5
I'm trying to find a way to segment the population based on the answers in the survey, so I can understand people's behaviors, their needs and frustrations in order to build personas. But I have no inkling about how to parse the data I've collected. If it were only a few responses, I could probably wing it, but with over 300 I'm at a loss. I've asked all the right questions, I just need to figure out how to turn this data into meaningful clusters of people who interact with the system in similar ways. Does that make sense?
 
#7
For a second there I thought I understood: I figured that the numbers in parentheses represented values in my columns. But the 8 and 9 threw me off. Am I looking at two segments? One that represents people who hardly ever create or edit menus and are not very tech or design savvy and another that represents people who create and edit menus often and are quite tech and design savvy?
 

noetsi

Fortran must die
#8
Its probably not a very helpful answer but I would run factor analysis :p Structural Equation models are even better, but that takes most years to learn.

I would also review the literature on this topic, someone may have already made suggestions. Generally you have a theory before you run the data.
 
#9
Factor analysis will help you condense the number of questions down into a smaller number of factors underlying the questions. Your data appear to condense to 2 factors of importance.

You can also go directly into Cluster analysis. I recommend Cluster Observations. This should accomplish what you requested. After a cursory look at your data, you should be able to easily condense these into 13 clusters of differing customer types.