### Thursday, December 29, 2005

## Re: RE: st: significance of model improvement using log pseudolikelihood?

dear Bart,

Testing for improvement in model between nested models fit is the same as testing constraints. In your case a very easy one, but sometimes the constraint can be very complicated as Stas pointed out. In many cases, especialy with easy constraints, looking at the test as a test of constraints is more substantively interesting than looking at the test as a test of improvement in fit. Our substantive interest usualy lies with wether variables x, y, and z have an effect or not (and if so how large the effect is), and not in whether model 1 fits the data beter than model 2. So my advise is to always keep the question you want to answer in mind, and choose your test and interpretation of the test accordingly. In many cases you will find the "test of constraints" interpretation more meaningfull than the "improvement of fit" interpretation, regardless of whether the difference between models is one or more variables / number of constraints is one or more.

A more specific answer to your question is that the p-value you reported is already from the wald test. So it would indeed be double to report the same test twice.

HTH, Maarten

----------------------------------------- Maarten L. Buis Department of Social Research Methodology Vrije Universiteit Amsterdam Boelelaan 1081 1081 HV Amsterdam The Netherlands

visiting adress: Buitenveldertselaan 3 (Metropolitan), room Z214

+31 20 5986715

http://home.fsw.vu.nl/m.buis/ -----------------------------------------

--- Bart Vanneste <bvanneste.phd2004@london.edu> wrote:

> In my case model 2 contains just one variable more than model > 1. Testing the increase in model fit would then be equivalent > to testing whether the coefficient is different from zero. > > From a practical viewpoint, is there any value in separately > reporting the significance in model fit given that I already > report the P-values of the individual coefficients? Or does it > only make sense when at least 2 variables are added in > subsequent models?

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