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st: Principal Components Analysis with count data

From   "Jason Ferris" <JasonF@TURNINGPOINT.ORG.AU>
To   <>
Subject   st: Principal Components Analysis with count data
Date   Fri, 14 Aug 2009 09:08:48 +1000

Thank you for your added comments; it gives me hope not to have to
categorise the data and use MCA methods.

To address your third point: 

3. Whether PCA will work well does depend on what you expect it to do
ideally, which is not clear in the question. 

We have a survey question of 2600 people.  Who answer 14 questions about
varying degrees of community harms.  Each question collects 'how many
times did this happen to you in the previous 12 months'.  So we collect
counts of all 14 questions - ranging from 0 to 365 (indicating at least
once a day).  The typical pattern for most of the items are as follows:
mean <2; 50 percentile (0); 75 percentile (0/1).  

We are hoping to reduce the 14 questions to 3/4 main components to use
as outcome variables for further analysis.

If we go down the MCA approach we would probably group the data 0, 1/5,


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