Timeline for Comparing a mixed model (subject as random effect) to a simple linear model (subject as a fixed effect)
Current License: CC BY-SA 2.5
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| Mar 17, 2011 at 20:39 | comment | added | ocram | @MudPhud: To the best of my knowledge, there is no p-value for such a decision. If interest centers on the effect of the specific levels chosen then it should be considered as fixed. If the available factor levels are seen as a random sample from a larger population and that inferences are wanted for the larger population, the effect should be random. | |
| Mar 17, 2011 at 20:34 | history | edited | ocram | CC BY-SA 2.5 |
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| Mar 17, 2011 at 20:28 | comment | added | MudPhud | I agree, but when I tried explaining this to my PI he just turned around and asked for a p-value of some kind. I want to include this analysis in a manuscript, but he won't put it in if there isn't a more concrete justification. | |
| Mar 17, 2011 at 20:24 | comment | added | ocram | @MudPhud: Modelling a variable as a fixed or as a random effect should actually be decided before the analysis, when the study is planned. It depends, in particular, on the scope of your conclusions. Random effects allow more generalisability. It could also avoid some technical difficulties. For example, the asymptotics might break down when the number of parameters grow up, as it is the case when a categorical variable with a lot of levels is considered as a fixed variable. | |
| Mar 17, 2011 at 20:16 | comment | added | MudPhud | The question is: is there are test to decide if the variable should be modeled as a mixed effect or random effect? Otherwise you could do the test you described and then test it with a chi-square dist (I'm not sure what the appropriate test would be). | |
| Mar 17, 2011 at 20:08 | history | answered | ocram | CC BY-SA 2.5 |