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From |
David Airey <david.airey@vanderbilt.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Anova |

Date |
Mon, 1 Dec 2008 19:43:56 -0600 |

-Dave On Dec 1, 2008, at 4:42 AM, aapdm wrote:

Dear John,Thanks for this, the gfields is indeed very useful! I am now usingit and it gives me pretty nice results. However I am surprised thatthe contribution of one of my explanatory variables is negative(although small). Any idea why this can be the case?Many thanks, Alice. --- On Fri, 28/11/08, John Antonakis <John.Antonakis@unil.ch> wrote:From: John Antonakis <John.Antonakis@unil.ch> Subject: Re: st: Anova To: statalist@hsphsun2.harvard.edu Date: Friday, 28 November, 2008, 4:32 PM Simply estimate: xi: reg y x1 x2 x3 x4 x4 x6 i.dumm, beta. If your variables are either binary or continuous variables then you can examine the beta coefficient (i.e., standardized) for relative impact. For other ways to look at coefficients download "listcoef". Also "gfields" is nice for breaking down variance. If you have categorical data with more than two categories use the "test" command to test whether the dummies of that variable dummies are jointly different from zero: e.g., test I_dumm1 I_dumm2 I guess you could convert that F or chi-square that it reports to an effect size. HTH, John Antonakis University of Lausanne Switzerland * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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**Follow-Ups**:**st: Newey west***From:*Alice <aapdm_999@yahoo.co.uk>

**References**:**Re: st: Anova***From:*aapdm <aapdm_999@yahoo.co.uk>

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