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Re: st: A reference for "how many independent variables one regression can have?"
From
Alan Acock <[email protected]>
To
"[email protected]" <[email protected]>
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 <[email protected]> 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
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/faqs/resources/statalist-faq/
>> * http://www.ats.ucla.edu/stat/stata/
>
> -------------------------------------------
> Richard Williams, Notre Dame Dept of Sociology
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>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/faqs/resources/statalist-faq/
> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
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* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/