Thanks for sharing your experience Alex,
but does anyone have a solution? Or I think this is a
problem of all IRT software? Anyway, how much can you trust
a software with such a peculiar behaviour?
Jason
----- Original Message Follows -----
From: "A. Beaujean" <[log in to unmask]>
To: [log in to unmask]
Subject: Re: Parscale
Date: Tue, 16 Jan 2007 13:30:38 -0600
> I have a similar thing happen in BILOG-MG when I have a
> large data set. When I put priors in the parameters, it
> sometimes works; likewise, changing the number of
> quadrature points.
>
> Alex
>
>
> On 1/16/07, Iasonas Lamprianou <[log in to unmask]>
> wrote: >
> > Dear friends,
> > I have been working with Parscale for some time, and I
> > was always anxious when the Newton Cycles (after the E-M
> > Cycles converge) did not converge (actually usually
> > diverge). This time, its the same story. The Newton
> > Cycles diverge. When I fiddle with the settings,
> > Parscale manages 2-3 cycles and then diverges again.
> > Anyone knowing any tricks to make this creature
> converge? >
> > Jason
> >
> > ----- Original Message Follows -----
> > From: Paul Barrett <[log in to unmask]>
> > To: [log in to unmask]
> > Subject: Very important paper on SEM modeling
> > Date: Fri, 5 Jan 2007 13:50:22 +1300
> > > Hello again
> > >
> > > Almost forgot - but I think this is a very important
> > > and readable paper for anyone contemplating using
> > > hierarchical factor models in SEM ... It's clearly
> > > written, and that nested (bifactor) model is a very
> > > nice way of modeling a general factor. I've used this
> > myself recently ... >
> > > Gignac, G. (2007) Multi-factor modeling in individual
> > > differences research: Some recommendations and
> > > suggestions. Personality and Individual Differences,
> > > 42, 1 , 37-48.
> > >
> > > Abstract
> > > This paper offers some commentary and recommendations
> > > relevant the multi-factor modeling in individual
> > > differences research. Several similarities and
> > > distinctions between oblique factor modeling,
> > > higherorder modeling, Schmid-Leiman transformations,
> > > and nested factors modeling are discussed. An
> > > empirical illustration of the various multi-factor
> > > models is presented, based on 18 items derived from
> > > three Neuroticism facets within the NEO PI-R.
> > > Researchers are encouraged to always perform a
> > > Schmid-Leiman transformation to a higher-order model
> > > solution, as well as to consider the possibility that
> > > a nested factors model will yield superior model fit,
> > > in comparison to a higher-order model, as well as less
> > ambiguous factor solutions. >
> > >
> > > Another recent paper on the same topic - but focused
> > > more in the Quality of Life literature is:
> > >
> > > Chen, F.F., West, S.G., and Sousa, K.H. (2006) A
> > > comparison of bifactor and second order models of
> > > quality of life. Multivariate Behavioral Research, 41,
> > 2, 189-225. >
> > > Abstract
> > > Bifactor and second-order factor models are two
> > > alternative approaches for representing general
> > > constructs comprised of several highly related
> > > domains. Bifactor and second-order models were
> > > compared using a quality of life data set (N = 403).
> > > The bifactor model identified three, rather than the
> > > hypothesized four, domain specific factors beyond the
> > > general factor. The bifactor model fit the data
> > > significantly better than the second-order model. The
> > > bifactor model allowed for easier interpretation of
> > > the relationship between the domain specific factors
> > > and external variables, over and above the general
> factor. Contrary to the literature, sufficient power
> > > existed to distinguish between bifactor and
> > > corresponding second-order models in one actual and
> > > one simulated example, given reasonable sample sizes.
> > > Advantages of bifactor models over second-order models
> > are discussed. >
> > >
> > > Regards .. Paul
> > > ___________________________________________________
> > >
> > > Paul Barrett
> > > Mob: +64-021-415625 www.pbmetrix.com
> > > <http://www.pbarrett.net/> Skype: pbar088
> > > [log in to unmask]
> > >
> > >
> >
>
>
>
> --
> ***************
> A. Alexander Beaujean, Ph.D.
> http://myprofile.cos.com/abeaujean
> http://www.baylor.edu/soe/faculty/index.php?id=38476
>
>
>
> "General impressions are never to be trusted.
> Unfortunately when they are of long standing they become
> fixed rules of life, and assume a prescriptive right not
> to be questioned. Consequently those who are not
> accustomed to original inquiry entertain a hatred and a
> horror of statistics. They cannot endure the idea of
> submitting their sacred impressions to cold-blooded
> verification. But it is the triumph of scientific men to
> rise superior to such superstitions, to devise tests by
> which the value of beliefs may be ascertained, and to feel
> sufficiently masters of themselves to discard
> contemptuously whatever may be found untrue." --Sir
> Francis Galton, FRS
>
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