John Ziker wrote:
> This research deals with the classical anthropological question of
> food sharing among hunters and gatherers. There are a number of
> hypotheses being discussed within the field. This study is relevant
> for two models, namely kinship cooperation and reciprocity. The
> kinship model predicts greater assymetry in sharing with increasing
> proximity of relatedness between the partners. The reciprocity model
> predicts that sharing is contigent on returned acts of sharing. I have
> a small sample of meals I observed and documented among Dolgan and
> Nganasan hunter-gatherers in a remote community in the Siberian
> Arctic. I documented approximately 800 meals in 1995 and 1996. Of
> these, 145 meals included members of more than one household. I am
> including the raw data in this message. These raw data are: the number
> of times household x hosted household y, the number of times household
> y hosted household x, and the average household relatedness of
> household x and y. The relatedness figure was calculated as the
> average relatedness (r) of each pair of individuals in each household.
> [The variable 'r'is used in biology to represent the likelihood that
> two individuals share a gene at a given locus.]
>
> The main question I have is: with these data is it possible to
> determine statistically whether or not average household r predicts x
> to y sharing better than y to x reciprocity, or vice versa. The sample
> is highly skewed because of the fact that, even though the households
> represented are the ones in my sample that had the highest number of
> sharing partners, not every household hosted each other.
...
>
> I have run Spearmans rho and the correlation is highly significant for
> all comparisons. The data are not normal though, and I am questioning
> multiple regression results (X to Y dependent variable). A college of
> mine suggests that the standardized beta result may be a valid
> indicator of some significant difference however. I'd greatly
> appreciate any suggestions.
Regression does NOT require normally distributed data. Neither the
independent nor the dependent variable needs to be normally distributed.
It is a common misconception that normality is required.
However, it is required that the errors from the prediction are normally
distribution. Generally, this is tested after you fit the regression by
seeing if your residuals are normally distributed.
Now, having said this, how does it apply to your sitaution? You need to
examine your data and see if the assumption holds. It may not, but I
won't presume to do the work for you.
Your question about using standardized betas is confusing to me, this is
just a different scaling of the betas, it doesn't affect significance.
--
Paige Miller
Eastman Kodak Company
[EMAIL PROTECTED]
"It's nothing until I call it!" -- Bill Klem, NL Umpire
"When you get the choice to sit it out or dance,
I hope you dance" -- Lee Ann Womack
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