[R-sig-ME] Relationship between mixed-effects models and fixed-effects models

[Phillip Alday]

And the relevant page is on Google Books:

https://www.google.com/books/edition/Data_Analysis_Using_Regression_and_Multi/lV3DIdV0F9AC?hl=en&gbpv=1&dq=gelman%20hill&pg=PA245&printsec=frontcover

This matches up with both my and James' comments.


On 07/06/2021 10:30, Phillip Alday wrote:
> Somewhat related to this and what James wrote, in the world of fMRI and
> other two-stage analyses in psychology and neuroscience, the "fixed
> effect" vs "random effect" distinction is used in the same sense as in
> meta-analysis, which lines up more closely with the use in mixed models,
> i.e. whether or not the individual estimates are treated as observed
> draws from a random variable in the group-level analysis.
>
> On 07/06/2021 10:27, Phillip Alday wrote:
>> If I understand correctly, "fixed effects" in econometrics are simply
>> categorical variables, especially ones with a large number of levels.
>> There are "fixed" in the sense that they are observed at fixed
>> (discrete) levels instead of as continuously.
>>
>> I don't have access to my copy at the moment, but this is discussed in
>> Gelman & Hill (2006).
>>
>> Phillip
>>
>> On 07/06/2021 10:09, Douglas Bates wrote:
>>> Occasionally I encounter discussions of what are called fixed-effects
>>> models in econometrics but I haven't seen descriptions of the underlying
>>> statistical model.  Can anyone point me to a description of these models,
>>> in particular a description in terms of a probability distribution of the
>>> response? I would be particularly interested in a discussion of how they
>>> relate to mixed-effects models as we think of them in lme4 and nlme.
>>>
>>> 	[[alternative HTML version deleted]]
>>>
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