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"CR1" Formula #22
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We use the definition from Cameron and Miller (2015, p. 9). There's not a clear right answer even for very simple cases because the degree of bias depends on whether the predictors are identified within each cluster or between clusters:
But I think the take-away is clear: CR1 (either version) doesn't account for the fact that the degree of bias adjustment depends on the structure of the predictors. Better to use CR2, which takes these features into account, instead. |
I agree that CR2 is better. I was just comparing results from |
I guess I could add "CR1p" that uses the factor m / (m - p). Is the rationale just compatibility/comparability with metafor?
…Sent from my iPhone
On Aug 17, 2017, at 4:24 PM, Wolfgang Viechtbauer ***@***.***> wrote:
I agree that CR2 is better. I was just comparing results from clubSandwich with robust() from metafor, where I use m / (m - p) by default. One can set adjust = "Stata2", which then uses m / (m - 1), but this is currently undocumented (and adjust = "Stata1" which is the same as CR1S, but see issue #23). Would you mind adding m / (m - p) as another option (as CR1<?>)?
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Indeed. Thanks for considering. |
…ion for CR1S option (closes issue #23).
Shouldn't the adjustment factor for "CR1" be
m / (m - p)
, wherem
is the number of clusters andp
the rank of the model matrix (instead ofm / (m - 1)
)? This would be analogous to 'HC1'. See, for example, MacKinnon and White (1985).The text was updated successfully, but these errors were encountered: