Re: st: A reference for "how many independent variables one regression can have?"

[Richard Williams <richardwilliams.ndu@gmail.com>]

A few comments:

* Long and Freese lay out some sample size suggestions for Maximum 
Likelihood Methods (e.g. logit) on p. 77 of
http://www.stata.com/bookstore/regression-models-categorical-dependent-variables/

I summarize their recommendations on pp. 3-4 of 
http://www3.nd.edu/~rwilliam/xsoc73994/L02.pdf .
* This paper claims that 10 may be more than you need:

http://aje.oxfordjournals.org/content/165/6/710.full.pdf

* I would say 10 cases per parameter rather than 10 cases per 
observation. With something like an mlogit model, you might estimate, 
say, 3 parameters for every independent variable.
* Like Richard Goldstein suggests, you may need a minimum number of 
cases. Long and Freese say you need at least 100 cases for a ML 
analysis. On the other hand, for something like a T test and the 
regression model equivalents of it, you can get by with some absurdly 
small number of cases if assumptions of normality are met. 
(Interesting tidbit: Counter to common practice, Long and Freese say 
you need to use more stringent p values when N is small, since the 
small sample properties of ML significance tests are not known).
* As a practical matter, I suspect you usually need much more than 10 
cases per parameter if you want to get statistically significant results.
At 10:50 AM 12/13/2013, Ariel Linden wrote:
> Hi All,
>
> I came across a statement in a book I am using to teach a class on
> evaluation that says "a common rule of thumb is that 1 independent variable
> can be added for every 10 observations." (it goes on to say that this
> depends on multicollinearity and desired level of precision). The book does
> not provide a reference for this statement.
>
> Does someone know of a reference for this ratio, or perhaps a different
> ratio?
>
> Thanks!
>
> Ariel
>
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