# Which statistical method to use for finding systematic patterns in data

As part of a broader study I am analysing 30 websites that fall into 3 categories:

• Consumer (10 sites)
• Commercial (10 sites)
• Health (10 sites)

The approach I used was a 'tick and flick' spreadsheet with 24 dichotomous variables that represent features of the website that are either absent or not (i.e. they receive a tick if they exhibit that particular feature).

Here is an example of the data.

The numbers represent how many websites from each category contain each particular feature (variable).

I want to know which kind of statistical test would be used to find if there are any systematic patterns about which 'Category' of website tends to correlate with particular variables. For example, which websites tend to share power with users to edit/contribute web content (measured by variables 2,3,4,5,6,7,8,13,16,19,23,24)?

I would rather use a more robust/rigorous statistical approach than simply counting up totals, or 'eye-balling' patterns in the data.

• the link is not working. also have you considered machine learning procedures ? Another approach is using entropy although i am not sure how would you do testing of traditional hypothesis testing nature with these. – htrahdis Oct 29 '13 at 14:29
• Hi @htrahdis, the link seems to be working for me? Sorry about that. Here is a simplified version of the data example: Category Consumer Commercial Health var1 4 1 0 var2 17 5 6 var3 5 7 9 var4 1 8 2 var5 7 11 4 var...n – timothyjgraham Oct 29 '13 at 22:09
• the data works now. but nowhere in the inputs is it given as to how to decide if a category has a particular feature which is a mixture of multiple features. you need to decide how much weightage to give to each variable when deciding a feature which is not present in the given features. – htrahdis Oct 30 '13 at 14:52
• @htrahdis Ok thanks. What if each variable simply has equal weight? – timothyjgraham Oct 30 '13 at 20:25