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

From   Richard Williams <>
To, <>
Subject   Re: st: A reference for "how many independent variables one regression can have?"
Date   Fri, 13 Dec 2013 12:10:11 -0500

A few comments:

* Long and Freese lay out some sample size suggestions for Maximum Likelihood Methods (e.g. logit) on p. 77 of

I summarize their recommendations on pp. 3-4 of .

* This paper claims that 10 may be more than you need:

* 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



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