Is it safe to say that Logistic regression using maximum likelihood will produce highly bias odds ratios? If so, what other options do I have? I came across Penalized Likelihood /Firth method, as well as Exact Logistic regression, but NONE of those are available on SPSS.

Do you think my best bet is just a Chi Square to estimate the contingencies between the DV and the IV's, and forget about my dreams of modelling their likelihoods? ]]>

I am writing a program that does Cool And Interesting Things starting with a correlation matrix among 3 variables: call them X, Y, and Z. I want the user to be able to specify the correlation matrix using

For example, one possibility would be that the user supplies the simple correlation between Y and X, the simple correlation between X and Z, and the partial correlation between Y and Z (controlling for X). The program should deduce the simple correlation matrix (i.e., convert the one partial correlation to a simple correlation) and then proceed from there.

The program should be able to handle any possible combination of inputs (as long as it ultimately specifies a valid simple correlation matrix.) There are 2^3=8 possible types of input, namely:

I have only the 2 of the 8 cases worked out. In the first case, obviously if the user just supplies the 3 simple correlations, then there is no problem to solve. In the last case, where the user supplies 3 partial correlations, I can obtain the simple correlation by basically reversing the procedure described HERE. But I am having a hard time working out the 6 more interesting cases. I wonder if anyone can help point me in the right direction. Thanks!

NOTE: I have cross-posted this question on CrossValidated here:

http://stats.stackexchange.com/quest...mples-partials

Please check for answers there before duplicating another's effort. ]]>