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


From   Nick Cox <n.j.cox@durham.ac.uk>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Identifying the best scale without a "gold standard"
Date   Thu, 17 Nov 2011 11:02:59 +0000

-clv- is from SSC. Please remember.... 

I'd add a puff for my own -eofplot- (also SSC) for looking at results. It is, curiously, a standard plot in some fields using components, factors, empirical orthogonal functions, but seemingly unknown in others. 

Nick 
n.j.cox@durham.ac.uk 

Ronan Conroy

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 struc- ture 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. 

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