Generalisability theory

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Generalisability theory

Patricia Rego
I should be grateful if someone would let me know of some literature (at "idiot" level) on the use of SPSS using generalisability theory.  I have a data set of assessment results from a number of sources and believe the latter to be an appropriate way to analyse the results reliably.

With thanks

Patricia RĂ©go
Evaluation Officer
School of Medicine
The University of Queensland
Ph: 61-7-33464683
Fax:  61-7-33655522
[hidden email]

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-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Hector Maletta
Sent: Saturday, 24 June 2006 11:15 PM
To: [hidden email]
Subject: Re: Brief Conceptual Question

Picky indeed, Richard. In my first point, of course I am referring to the
same result, only obtained from two samples of different size. In my second
one, the difference is immaterial for the question asked. In both a priori
and a posteriori interpretations the idea is the same for the purpose of the
question. I tried to keep my answer as simple as possible for the benefit of
our colleague asking the question, who may be confused by too many niceties.

-----Mensaje original-----
De: Richard Ristow [mailto:[hidden email]]
Enviado el: Saturday, June 24, 2006 1:48 AM
Para: Hector Maletta; [hidden email]
Asunto: Re: Brief Conceptual Question

To be a little picky -

At 11:26 PM 6/23/2006, Hector Maletta wrote:

See phrase in brackets and caps:

>1. The larger the sample, the greater the statistical significance of
>UNEXPLAINED VARIANCE IN THE DATA>. This means that the larger the
>sample, the lower the chance that the result is just a fluke or chance

>2. If F is above a certain minimum value, you can bet (with a certain
>degree of confidence) that the proportion of variance explained by
>your model is not zero.

Alas, not so; confidence levels tell you something different, and much
less satisfying. What Hector is describing is called the *a posteriori*
probability that you have a false positive result THIS TIME. The
significance level is the *a priori* probability of getting a result
this strong, in the absence of any true underlying effect.