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RE: st: Identifying the best scale without a "gold standard"


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Identifying the best scale without a "gold standard"
Date   Thu, 17 Nov 2011 13:04:41 -0500

What does the goodness-of-fit chi-square test say about the tenability of a one- or two-factor solution?Cam

> From: paul.seed@kcl.ac.uk
> To: statalist@hsphsun2.harvard.edu
> Date: Thu, 17 Nov 2011 14:21:11 +0000
> Subject: Re: st: Identifying the best scale without a "gold standard"
> 
> Thank you Ronan (and Nick) for pointing me to some useful software. 
> And Jean-Benoit for writing it.
> 
> I agree about -clv- . It identified a single factor as the best 
> representation, but also suggested a possible second 
> factor, based on two scales with a higher than average 
> correlation.  All with the simplest possible format.  
> 
> BW
> 
> 
> Paul Seed
> 
> 
> Ronan Conroy <rconroy@rcsi.ie> wrote: 
> 
> Date 	  Thu, 17 Nov 2011 10:48:26 +0000
> 
> >>On 2011 Samh 16, at 18:15, Cameron McIntosh wrote:
> >> Hayton, J.C., Allen, D.G., & Scarpello, V. (2004). Factor Retention Decisions in Exploratory Factor Analysis: a Tutorial on Parallel Analysis. Organizational >>Research Methods, 7(2), 191-205.http://orm.sagepub.com/content/7/2/191.full.pdf+html 
> 
> >A very well worthwhile article. The authors make the point that "Specifying too few factors results in the loss of important information by ignoring a factor or >combining it with another (Zwick & Velicer, 1986). This can result in measured variables that actually load on factors not included in the model, falsely loading >on the factors that are included, and distorted loadings for measured variables that do load on included factors. Furthermore, these errors can obscure the true >factor structure and result in complex solutions that are difficult to interpret (Fabrigar et al., 1999; Wood, Tataryn, & Gorsuch, 1996)."
> >
> >I really like Jean-Benoit Hardouin's -clv- command in this context, giving a splendid visual display of the structure of the items. It has revealed important >features of data, such as factors-within-factors, that would have been far harder to spot in the output of any factor analytic command. 
> 
> 
> 
> Ronán Conroy
> rconroy@rcsi.ie
> Associate Professor
> Division of Population Health Sciences
> Royal College of Surgeons in Ireland
> Beaux Lane House
> Dublin 2
> 
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