Hi Thom
The reason to divide by 2 SD is that it makes a continuous variable
comparable to a binary variable.
On 29 January 2010 00:22, Thom Baguley <[log in to unmask]> wrote:
> On Wed, 27 Jan 2010 09:30:45 -0800, Jeremy Miles <[log in to unmask]> wrote:
>
>>You can standardize (or divide by 2 SDs - Gelman recommends that in a
>>paper somewhere, and in his regression book), or somehow equate the
>>scales. Or you can try to make a sensible comparison - e.g. in one
>>analysis I did, I said that smoking is equivalent to being 8 years
>>older, in terms of the odds of having some condition (I think it was
>>Dupuytrens).
>>
>>However, if you standardize, you throw away the original information
>>about the scales, which can be a problem, because your comparison is
>>sensitive to the SD of the data in your sample.
>
> I'm not a fan of standardization for these reasons, but Gelman's position seems to be to
> semi-standardize by just rescaling the predictors as a default in this way. This is less
> problematic than also standardizing the outcome variable. An equivalent scaling is
> obtained by using effect coding for categorical predictors and dividing by 1 SD.
>
> Another scaling is to use percentage or proportion of maximum performance (POMP). This
> doesn't get distorted by the bias in estimating the SD or by reliability etc. However, it
> requires that you know the min and max possible.
>
> However, this doesn't really get around the problem of comparability - which is more of a
> philosophical problem. I'd consider reversing it so you can see how much change in a
> continuous predictor is required to produce the same OR change as your dichotomous
> variables. This will get clumsy of you have lots of dichotomoous predictors.
>
> Best of all you can try and graph the effects - as a good plot can often reveal the
> complexity in the data.
>
> Thom
>
--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com
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