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

______________________________

Patricia RĂ©go

Evaluation Officer

School of Medicine

The University of Queensland

Ph: 61-7-33464683

Fax: 61-7-33655522

[hidden email]
========================================================

The Breast Cancer Site is back and needs your help in funding mammograms for women in need.

Help make early detection possible every day with a simple click, at no cost to you.

Visit

http://www.thebreastcancersite.com today!

-----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.

Hector

-----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

>a statistical result <OF THE SAME OBSERVED MAGNITUDE, WITH THE SAME

>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

>occurrence.

>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.