Timeline for How to handle ordinal categorical variable as independent variable
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| Apr 18, 2016 at 10:19 | comment | added | ttnphns | @kjetilbhalvorsen, Yes it is possible, thank you. This option however is already implied in Pt 2 because one of the methods of optimal scaling for ordinal variables uses spline. | |
| Apr 18, 2016 at 8:45 | comment | added | kjetil b halvorsen♦ | Another option is to use an aditive model, and represent the ordinal independent variable via a spline. | |
| Feb 12, 2016 at 17:07 | history | edited | ttnphns | CC BY-SA 3.0 |
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| Feb 12, 2016 at 17:01 | history | edited | ttnphns | CC BY-SA 3.0 |
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| Feb 12, 2016 at 16:50 | history | edited | ttnphns | CC BY-SA 3.0 |
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| Feb 12, 2016 at 16:30 | comment | added | ttnphns | @Scortchi, Thank you for the comment. Regarding (2) - yes, in particular, it is of course more reliable to do optimal scaling on on a separate subset of the data on which the final regression will be done. (3) - thanks, too, I'll get myself acquainted with it. | |
| Feb 12, 2016 at 16:18 | history | edited | ttnphns | CC BY-SA 3.0 |
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| Feb 12, 2016 at 16:15 | comment | added | Scortchi♦ | (+1) (1) You can also use only the first few polynomial contrasts if you think they're enough. (2) Defining predictors from the response in the same data set should come with a health warning. (3) You can also penalize discrepancy between the coefficients of adjacent levels - see stats.stackexchange.com/q/77796/17230. | |
| Feb 12, 2016 at 15:59 | history | edited | ttnphns | CC BY-SA 3.0 |
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| Feb 12, 2016 at 15:46 | history | answered | ttnphns | CC BY-SA 3.0 |