Thank you so much for all your answers! And sorry for being scarce on the details. My dataset has 12 variables (6 ordinal coded from 1 to 5, and 6 binary) and 384 cases without missing value. High values mean 'positive' attitude toward the object of study.
I probably went too fast in my earlier impression that the variables' distribution were almost symmetrically opposed. I got confused by the high frequency of the combination (0, 1), sorry. Here is the crosstab: Observed counts x2 x1 0 1 0 23 0 1 334 27 Expected counts x2 x1 0 1 0 21.38281 1.617188 1 335.61719 25.382812 The actual counts for (0, 0) and (1, 1) being slightly above the expected counts, I can now understand the positive correlation. But does the high polychoric correlation make sense when the variables are so skewed and the difference between the actual and expected counts of the crosstab is so small? Regarding the difference of correlation coefficient between x1 and x2 with polychor and hetcor: I used 'hetcor' (polycor package) with 2-step and ML estimations on the whole dataset. The data were first declared as 'factor' otherwise hetcor would just compute Pearson correlations. hc = hetcor(thedata,ML=F, std.err=F) (correlation x1x2) 0.8013 hc = hetcor(thedata,ML=T, std.err=T) "Error in solve.default(result$hessian) : Lapack routine dgesv: system is exactly singular". Using polychor with the 2-step, and ML estimates: polychor(x1,x2, ML=F, std.err=F) [1] 0.9330044 polychor(x1,x2, ML=T, std.err=T) "Error in solve.default(result$hessian) : Lapack routine dgesv: system is exactly singular". Murray, you mentioned that the correlation between my two variables could be affected by other variables, hence the difference between polychor (on only two variables) and hetcor (on all the variables). I run polychor and hector on created variables (correlated and not correlated). Although I thought that the heterogeneous correlations were run only within each pair of variables (therefore, not being affected by other variables), a third variable correlated with x1 and x2 does slightly affect the correlation between x1 and x2. Thanks for this suggestion. I need to look better into the computation of polychoric correlations…! -- View this message in context: http://www.nabble.com/polychoric-correlation%3A-issue-with-coefficient-sign-tp21425977p21448444.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
