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Optimal procedures may differ depending on how many ordinal variables you have, how widely the number of categories differs from variable to variable, and whether all variables are of equal importance. The following tentative suggestion may work best for a few variables of equal importance and numbers of categories between 3 and 7. That is, labels $1,2,3$ (...


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@chl is right. I added it to to the function output to remind persons what hypothesis they are testing because it is often not clear what the alternative ($H_A$) and the null hypothesis ($H_0$) is. So it just tells you what the null hypothesis is and nothing about the acutal result. p < 0.05 means that $H_0$ can be rejected. So in your case the parallel ...


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Asked for help too soon sorry, I found the solution in Olsson (1979, p.447): The joint probability $p_{ij}$ can be derived via: $p_{ij} = \Phi_2(a_i,b_j) - \Phi_2(a_{i-1},b_j) - \Phi_2(a_i,b_{j-1}) + \Phi_2(a_{i-1},b_{j-1})$ Where $a_i,b_j$ are the thresholds and $\Phi_2$ is the bivariate normal PDF with (polychoric) correlation $p$ Olsson, U. Maximum ...


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I don't have enough reputation to comment. But let me give you my view. Ordinal data is related to information organized in a particular order without indicating a specific relationship between each item. Items may be greater than or less than other items. The order of items is often defined by assigning numbers to them to show their relative position. ...


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