Dear Amy, What type of correlation do you mean? If it is an R of R^2 from a regression model, the fisher transformation is no longer applicable, as these outcomes lie between 0 and 1 (`real' correlations lie between -1 and 1). In addition, why use an ln tranformation? It does only bring your R to a distribution which is also suffering from truncated scale (-inf to 0). To obtain standard errors of the multiple correlations, Wishart?s (1931) approximation of the variance of multiple R^2 may be used (also, see, Olkin & Finn, 1995, p. 161). Since squared multiple correlations can only be positive, the hypothesis of these parameters being equal to 0, should be tested against a one-sided alternative.
Hope this helps Niels Smits -----Oorspronkelijk bericht----- Van: [email protected] namens A Rangel Verzonden: vr 18-1-2008 5:58 Aan: [email protected] Onderwerp: [Impute] conundrum combining pearson r and r-squared Hello all, I am hoping to get some expert opinions on a relatively simple problem. Suppose that you want to combine Pearson r and r-squared values from 20 imputed data sets. It seems that standard advice (in small to moderate samples) is to first transform r using Fisher's (1915) r-to-z transformation. Similarly, ln(r-squared) seems to be an appropriate transformation. After combining and back-transforming, it is quite possible -- perhaps likely -- that you get inconsistent estimates. What I mean by that is that squaring the combined Pearson r value can be quite different from the combined R-square value that you get from back-transforming ln(R-sq). The fact that these two estimates does not surprise me. However, I'm looking for some practical advice on how to deal with this. How might you explain the discrepancy in a manuscript, for example? Is there a better way to deal with this (e.g., report the combined r value and square it to get a value of r-squared rather than combine the r-squared values separately). Any opinions on this would be much appreciated. Amy Rangel ____________________________________________________________________________________ Never miss a thing. Make Yahoo your home page. http://www.yahoo.com/r/hs _______________________________________________ Impute mailing list [email protected] http://lists.utsouthwestern.edu/mailman/listinfo/impute
