[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: ICC vs. shared variance from common factor analysis

From   Mike Lacy <[email protected]>
To   [email protected]
Subject   st: ICC vs. shared variance from common factor analysis
Date   Thu, 10 Jan 2008 12:12:01 -0700

I recently found myself puzzled by the wide disparity of a) the value of the intraclass correlation calculated after doing running -glamm- on several 5 point Likert items vs. b) the estimate of shared variability from a factor analysis of the -polychoric- correlation matrix for those several items. While I would not expect these two measures be identical, I was surprised to find them differing by more than a factor of two. The same phenomenon is easily reproduced for continuous response variables. For example, for the following synthetic data, the proportion of shared variation associated with the first factor is 0.93, while the ICC = 0.41.

set seed 49847
set obs 500
gen x = invnorm(uniform())
forval i = 1/5 {
gen y`i' = 1 + `i' * x + (`i' * invnorm(uniform()))
factor y*, ipf
gen id = _n
reshape long y, i(id) j(j)
loneway y id

Can anyone offer some enlightenment here? I'd like to understand why these approaches to common variability would diverge so sharply, with the practical end of gaining a sense of when/whether to use the ICC vs. the factor analysis results as an indication of the internal consistency of a set of measurements.


* For searches and help try:

© Copyright 1996–2024 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index