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st: RE: Principal Components Analysis with count data

From   "Anderson, Bradley" <>
To   "" <>
Subject   st: RE: Principal Components Analysis with count data
Date   Fri, 14 Aug 2009 09:50:13 -0400

After reading a description of your data I'd suggest you might want to take a look at the MPlus program.  It allows estimation of a wide range of models, including both exploratory and confirmatory factor analysis with continuous, count, ordered discrete, and dichotomous observed indicators.

-----Original Message-----
From: [] On Behalf Of Jason Ferris
Sent: Thursday, August 13, 2009 7:09 PM
Subject: st: Principal Components Analysis with count data

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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