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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 >> >> * >> * 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 > OFFICE: (574)631-6668, (574)631-6463 > HOME: (574)289-5227 > EMAIL: Richard.A.Williams.5@ND.Edu > WWW: http://www.nd.edu/~rwilliam > > * > * 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: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: A reference for "how many independent variables one regression can have?"***From:*"Ariel Linden" <ariel.linden@gmail.com>

**Re: st: A reference for "how many independent variables one regression can have?"***From:*Richard Williams <richardwilliams.ndu@gmail.com>

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