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From |
Richard Williams <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Re: Partial R-squares |

Date |
Wed, 15 Sep 2004 07:40:25 -0500 |

At 04:08 PM 9/14/2004 -0500, Yulia Marchenko wrote:

Brief discussions of partial and semipartial correlations can be found atThe partial R-square (or coefficient of partial determination) measures the marginal contribution of one explanatory variable when all others are already included in the model. For example, in the above example only 7.7% of the variation in mpg is reduced by adding weight to the model when length is already in the model. However, such interpretation is not valid, for example, if your explanatory variables are collinear.

http://www.nd.edu/~rwilliam/stats1/x94.pdf

http://www.nd.edu/~rwilliam/stats1/Semipartials-Stata.pdf

Better still is to read the Cohen and Cohen book that is cited in the notes (the notes use the 1975 version of the book but a newer edition was released in 2002 or 2003.)

This is one of those things I tend to teach "because it is there", i.e. it shows up in computer printouts and some authors may use them but it is not something I find terribly useful myself. It does help to make it clear why adding more variables to a model may produce little change in R^2.

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Richard Williams, Notre Dame Dept of Sociology

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**References**:**st: Re: Partial R-squares***From:*"Yulia Marchenko" <[email protected]>

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