Hi,
Unfortunately ran into a new problem .... When trying to perfom a linear regression analysis between two variables within a huge dataset I get a strange "Object ".osijfoiödjfoösdjfosjofsd" not found" (the letters are just an example). Could this maybe have to do with the filter problem again? Before performing the analysis I exclude all missing values with a "xxx !=0" filter (I know it's bad practice to make missing = 0, but in this very dataset 0 is equal to missing)
However, when I copy the data of the two variables and paste them into a new jamovi dataset and do exactly the same it works flawlessly. I don't know what happens here but it brakes my heart that I needed to use SPSS in between
Hi,
thanks for the quick answer! Gonna send you the file by email!
I think I now know how to reproduce the error and go back again: I had computed a variable I wanted to use for linear regression and got the error... to make sure it didn't have to do with the computed variable I copied the content of the computed variable into a new variable... but then got the same error... when I after this remove all the analysis leading to the error I can use the data from the latter named new variable and another one for regression w/o problems.
I run jamovie 0.9.2.3
Oh wait, I just saw that my version is very outdated... I'm running jamovi on linux and thought the repository just takes care of updates but seems I've missed a lot of them since you guys moved to flathub
all right, now updated to version 9.4.0 via flathub and everything works flawlessly.... sorry for the trouble and great this was already fixed!
edit: too bad... after the update and starting the new version everything went fine, but when trying to perform the next linear regression the same error appears again
.... When trying to perfom a linear regression analysis between two variables within a huge dataset I get a strange "Object ".osijfoiödjfoösdjfosjofsd" not found" (the letters are just an example). Could this maybe have to do with the filter problem again? Before performing the analysis I exclude all missing values with a "xxx !=0" filter (I know it's bad practice to make missing = 0, but in this very dataset 0 is equal to missing)