Estimating priors for Bayesian analysis

I've gone to a lot of trouble to add Bayesian adjustment in a spreadsheet for estimating confidence limits of an individual's true score when the subject is assessed with a noisy test. I specify the prior belief simply by stating a best guess of the true score, and its x% likely limits, with

Re: Student's t vs. z tests

At 1:18 PM -0500 23/4/01, Jon Cryer wrote: These examples come the closest I have seen to having a known variance. However, often measuring instruments, such as micrometers, quote their accuracy as a percentage of the size of the measurement. Thus, if you don't know the mean you also don't know

Error term in repeated-measures ANOVA

I do all my repeated measures analyses with mixed modeling in SAS these days, but I get called on to help people who use standard repeated-measures analyses with other stats packages. So here's my question, which I should know the answer to but I don't! In a repeated-measures ANOVA, most

Re: calculating reliability

The best measure of reliability is the standard error of measurement. It's really the same as the within-subject standard deviation (SD you expect to get when retesting a subject many times), but you take out any change in the mean between trials. For any reasonable sample size and two

Re: One tailed vs. Two tailed test

Responses to various folks. And to everyone touchy about one-tailed tests, let me make it quite clear that I am only promoting them as a way of making a sensible statement about probability. A two-tailed p value has no real meaning, because no real effects are ever null. A one-tailed p

Speaking of ANOVA in SPSS...

, they ain't there. Or did I miss something? If so, please let me know. And can you let me know of any simple, and preferably CHEAP or FREE, packages that will do what I want? Will -- Will G Hopkins, PhD FACSM University of Otago, Dunedin NZ Sportscience: http://sportsci.org A New View of Statistics

Re: One tailed vs. Two tailed test

ample size you would (probably) need to get a clear-cut effect. I can explain, if anyone is listening... Will -- Will G Hopkins, PhD FACSM University of Otago, Dunedin NZ Sportscience: http://sportsci.org A New View of Statistics: http://newstats.org Sportscience Mail List: http://sportsci.org/

Re: The meaning of the p value

I've been involved in off-list discussion with Duncan Murdoch. At one stage there I was about to retire in disgrace. But sighs of relief... his objection is Bayesian. OK. The p value is a device to put in a publication to communicate something about precision of an estimate of an effect,

The meaning of the p value

At 4:17 PM -0600 30/1/01, Jay Warner wrote: A technically correct conclusion is: The sample of 100 has a mu different than 100. there is a 0.08 prob ability (or 0.02, or 0.008) that this statement is false. Have I not said the same thing? As p gets small, we are more confident that the

Re: change scores (and more on regression to the mean)

My response is about regression to the mean generally, which got done over a little over a week ago. It occurred to me recently that you could reduce the regression-to-the-mean effect by using the subjects' least-squares means to divide them (the subjects) up into quantiles for separate

Re: Effect statistics for non-normality

Here's a response to the two people who have replied to the list about my query. (Thanks heaps for your input. This list is wonderful. If it ever loses its institutional support, and noone else wants to pick it up, I will. I'd run it with listproc, and we would have moderators to filter

Effect statistics for non-normality

y normal. Of course, if there is no reason to suspect non-normality of residuals, it's reasonable to use parametrics, even if the residuals in the small sample look non-normal (because, you would reason, they look non-normal only because of sampling error). Comments? Will -- Will G Hopkins,

Re: Effect size for heritability

Rich, thanks for those comments. I have a few remarks in reply. If you have a criterion (reaction time, etc.) where you average dozens or hundreds of observations to make a point to be analyzed, the "effect size" is magnified by averaging. That is, if you can change an average by .01, that

Effect size for heritability

and environmental contributions to the acquisition of a motor skill. Nature 384, 356-358 Will -- Will G Hopkins, PhD FACSM University of Otago, Dunedin NZ Sportscience: http://sportsci.org A New View of Statistics: http://newstats.org Sportscience Mail List: http://sportsci.org/forum ACSM Stats Mail List