Timeline for How should I analyze data with repeated measures variables and integer between-subject factors?
Current License: CC BY-SA 3.0
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| Jul 23, 2012 at 19:22 | comment | added | Sitsig | thanks for your comments. I felt that simply changing the scale was weird, but wasn't sure enough. The fact that I've seen some papers do it didn't help. I'm reading up on multi-level modelling right now. | |
| Jul 23, 2012 at 18:31 | history | edited | John | CC BY-SA 3.0 |
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| Jul 23, 2012 at 18:28 | comment | added | John | The scores might be converted when they're response variables because they're known not to be distributed normally and the arcsine fixes that. But there's not requirement for normality as a predictor variable. | |
| Jul 23, 2012 at 18:18 | comment | added | John | Converting it to another scale doesn't make it continuous. That's actually kind of an absurd idea. Then you'd just have intervals in a different scale. You have a variable that you can treat continuously as long as you don't take fractional scores as meaningful in discussion. In practice no variables are continuous since there is always some limit to the ability to measure. This is a relatively course continuous variable is all. | |
| Jul 23, 2012 at 15:06 | vote | accept | Sitsig | ||
| Jul 23, 2012 at 14:58 | comment | added | Sitsig | Thank you for a helpful response. As for using a continuous factor, I have seen some papers where the authors convert the integer scores in this test (MRT-A, referenced in a comment on the original post) to percentages (or an arcsine in one case) to make it continuous. But is that really enough? I will also look into multi-level modelling, and thanks for bringing that up. | |
| Jul 23, 2012 at 14:04 | history | answered | John | CC BY-SA 3.0 |