Thanks,
do you happen to know his email address?
Jason
----- Original Message Follows -----
From: "A. Beaujean" <[log in to unmask]>
To: [log in to unmask]
Subject: Re: Parscale
Date: Tue, 16 Jan 2007 14:11:52 -0600
> You can contact Leo Stamm (Sp?) at SSI about the issue. He
> does a good job at getting back with you, but as far as I
> can tell, it is just part of the problem with the SSI IRT
> suite. That is, it requires a lot of monkeying around to
> get convergence on some data sets.
>
> On 1/16/07, Iasonas Lamprianou <[log in to unmask]>
> wrote: >
> > 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
> > >
> >
>
>
>
> --
> ***************
> 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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