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


From   Alan Acock <acock@mac.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: A reference for "how many independent variables one regression can have?"
Date   Fri, 13 Dec 2013 09:55:56 -0800

Stata has excellent power tools that would be more useful than any single rule of thumb. 
Alan Acock

Sent from my iPad

> On Dec 13, 2013, at 9:10 AM, Richard Williams <richardwilliams.ndu@gmail.com> wrote:
> 
> 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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> 
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
> OFFICE: (574)631-6668, (574)631-6463
> HOME:   (574)289-5227
> EMAIL:  Richard.A.Williams.5@ND.Edu
> WWW:    http://www.nd.edu/~rwilliam
> 
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