True about Mplus, but I think what Jason is looking for is a formative model that rolls up the items into a composite harm index, rather than a reflective modeling framework that uses the items as indicators of an underlying latent harm construct. For that, generalized structured components analysis or partial least squares modeling might be better options:
http://www.sem-gesca.org/reference.php
http://faculty.chass.ncsu.edu/garson/PA765/pls.htm
(see PLS path modeling links near the bottom of the second link, that's what you'd want)
Cam
----------------------------------------
> From: [email protected]
> To: [email protected]
> Date: Fri, 14 Aug 2009 09:50:13 -0400
> Subject: st: RE: Principal Components Analysis with count data
>
> 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: [email protected] [mailto:[email protected]] On Behalf Of Jason Ferris
> Sent: Thursday, August 13, 2009 7:09 PM
> To: [email protected]
> Subject: st: Principal Components Analysis with count data
>
> Nick,
> 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
> 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,
> 6+.
>
> HTH,
> Jase
>
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