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. |
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