Not sure that is the best way to do this.

- Thread starter noetsi
- Start date

Not sure that is the best way to do this.

This is what I mean (see link). And I am not saying you have to code it in R - but this is how they can have random intercepts and slopes, which allows you to tease our the differences.

http://mfviz.com/hierarchical-models/

Would it be correct to specify an interaction effect between disability type and spending on a service to test this?

But in my view, you do not have to think about it (the disabilities) as a random effect. It could be a fixed effect. It depends on how you think about it. I mean you have not randomly selected these disabilities, but rather you have this list of disabilities. That is fixed. But you can still test for different intercepts and different slopes.

you do not have to think about it (the disabilities) as a random effect. It could be a fixed effect.

It is my understanding that you can get category intercepts and slopes from both process, but the MLM will address the within and between variability. I think of MLM as addressing that groups have a different data generating process composed of different coefficients that you are controlling for the similarities within the groups. It seems like you are referring to the theory when one should be select - and I would like to hear more about that!

Yes. I think that it would be very natural to investigate that possibility.

But in my view, you do not have to think about it (the disabilities) as a random effect. It could be a fixed effect. It depends on how you think about it. I mean you have not randomly selected these disabilities, but rather you have this list of disabilities. That is fixed. But you can still test for different intercepts and different slopes.

But in my view, you do not have to think about it (the disabilities) as a random effect. It could be a fixed effect. It depends on how you think about it. I mean you have not randomly selected these disabilities, but rather you have this list of disabilities. That is fixed. But you can still test for different intercepts and different slopes.

Talk more about this and the selection between the two options.

It is my understanding that you can get category intercepts and slopes from both process, but the MLM will address the within and between variability. I think of MLM as addressing that groups have a different data generating process composed of different coefficients that you are controlling for the similarities within the groups. It seems like you are referring to the theory when one should be select - and I would like to hear more about that!

It is my understanding that you can get category intercepts and slopes from both process, but the MLM will address the within and between variability. I think of MLM as addressing that groups have a different data generating process composed of different coefficients that you are controlling for the similarities within the groups. It seems like you are referring to the theory when one should be select - and I would like to hear more about that!

you do not have to think about it (the disabilities) as a random effect. It could be a fixed effect.

Talk more about this and the selection between the two options.

I would say that this is about how you think about your model.

Maybe this is similar to that I have an extra motivation for why you should randomize your experiment. Randomization will reveal what restrictions you have on randomization. "So you don't want to do a completely randomized design. You want a randomized block design. Fine!" Or: "you don't want to do a completely randomized design. You want a split plot design - a sort of multilevel design. Good to know!"

It seems like you are referring to the theory when one should be select

when one should be select

you do not have to think about it (the disabilities) as a random effect. It could be a fixed effect.

Talk more about this and the selection between the two options.

I would say that this is about how you think about your model.

Maybe this is similar to that I have an extra motivation for why you should randomize your experiment. Randomization will reveal what restrictions you have on randomization. "So you don't want to do a completely randomized design. You want a randomized block design. Fine!" Or: "you don't want to do a completely randomized design. You want a split plot design - a sort of multilevel design. Good to know!"

It seems like you are referring to the theory when one should be select

when one should be select

Or if you sample 100 fields and treat different subplots with different levels of nitrogen.

But if you have say all the hospitals that there is in the state (and that is all you are interested in) then I don't think that it is wrong to use the random effect model in a mixed model. I just guess that there would be a larger inference space if you think of it as a random effect. At least an author Anderson talked like that in an old book. Maybe the hospital can be thought of coming from a superpopulation model.

So the model is income = countofservicetype1, disab1 ,disab2,disab3,disab4 (and many other control variables). Do I build the interaction as income =countofservicetype1,disab1, countofservicetype1*disab1, disab2,countofservicetype1*disab2....and so on?

All the examples of interaction I have ever run into roll up what they are interested into one variable and specify an interaction with that rather than break it down into its categories and then analyze interaction.