Not exact matches
Ignoring the stratified sampling does not affect point estimates and may have resulted in slightly overestimated
standard errors.14
Robust variance estimation was
used to allow for the clustered nature of the data within units and trusts.
To gain insight into what brain regions may be driving the relationship between social distance and overall neural similarity, we performed ordered logistic regression analyses analogous to those described above independently for each of the 80 ROIs, again
using cluster -
robust standard errors to account for dyadic dependencies in the data.
I
use a statistical method known as
robust linear regression with countries as strata and schools (or countries where appropriate) as the primary sampling unit to calculate appropriate
standard errors for my findings and to adjust for this potential bias.
The factor regression tool supports the
use of
robust standard errors based on the Newey — West estimator.
We then
used logit models with
robust standard errors for our tests.97
Robust standard errors were
used to account for the clustering of children in schools for all models.
Robust variance (sandwich - type) estimates were
used to adjust the
standard errors of the parameter estimates for the stratified design effects.
This was tested
using logistic regression (with
robust standard errors) to model the extent to which wave 1 demographic characteristics and outcomes affect the probability of a family participating at wave 3.
In order to correct for the deflation of
standard errors and widened confidence intervals imposed by the dyad - level analysis, a cluster
robust function was
used to obtain individual specific confidence intervals.
Twins as a paired event were accounted for
using robust estimation of
standard errors and were not imputed, as there was no missing data for twins.