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Re: st: PCA or SEM for 2-indicator latent?


From   Nick Cox <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: PCA or SEM for 2-indicator latent?
Date   Thu, 9 Jan 2014 12:47:36 +0000

"while SEM is generally preferred to PCA"

This is like saying "cars are generally preferred to boats".

For what? If it's a flood out there, I'd prefer a boat. Less
obliquely, if you want a modelling technique, as you seem to do, PCA
is not a modelling technique. You might use PCA results in a model,
but that's a different story.

Nick
[email protected]


On 9 January 2014 02:40, Lin, Tin-Chi <[email protected]> wrote:
> Dear Statalisters,
>
> My data is longitudinal and I am facing a catch-22 in choosing a method to (1) construct the explanatory variable (let’s call it S), and (2) perform the regression modeling overall. SEM (structural equations modeling) and PCA (principal component analysis) are among the methods that I am considering.
>
> The dilemma is that while SEM is generally preferred to PCA (see a short but good summary from http://www.stata.com/statalist/archive/2012-09/msg01050.html), there is only two indicators (S1, S2) available for S in each wave. From other website, I learned that where there is only two indicator for a single latent, the regression model will be very sensitive to model mis-specification (http://www.statmodel.com/discussion/messages/11/4965.html?1261084141), and I think the problem will get worse in a longitudinal setting.
>
> Another question is, if I am going to use SEM and the primary explanatory variable is latent, is it possible to run a fixed-effect-like model to control for between-individual differences? I had this thought, because at the very beginning my plan was to ran PCA, get the prediction for S, and then use fixed-effects model to get rid of “contamination” from the unobserved differences. I know we can “translate” a fixed-effects model to SEM when all the x-variables are indicators, but I am not sure if we can still do so when an explanatory variable is latent.
>
> Thanks very much
>
> Tin-chi
>
>
> Tin-chi Lin
> Liberty Mutual Research Institute for Safety
>
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