Hi
I am working on svyset for a multilevel model that accounts for individuals clustered within countries. I am using cross-sectional data from European Social Survey (ESS) round 7. I've spent quite some time on understanding how to -svyset- data for valid inference. I am still somewhat uncertain about my strategy and comments are welcome.
I have merged round 7 with the corresponding sampling data file found here. I have the following sample data variables:
To -svyset- for multilevel, the procedure on this Stata page looks intuitive, i.e. individuals within schools:
I am not sure how this translates to the multilevel structure in my data with individuals within countries. I have tried:
I am uncertain about this specification, and when I run a multilevel model with the -svy:- prefix, I find it a bit odd that some estimates are not reported (included variables in model not meant to make sense as I am just testing for potential errors here):
(Note: This is a slightly edited repost from another thread as I thought this question was on a different topic than the original post. I have done some research on this topic and thought this post might be of relevance to other people with similar aims. Related post on weighting multilevel models with ESS here)
I am working on svyset for a multilevel model that accounts for individuals clustered within countries. I am using cross-sectional data from European Social Survey (ESS) round 7. I've spent quite some time on understanding how to -svyset- data for valid inference. I am still somewhat uncertain about my strategy and comments are welcome.
I have merged round 7 with the corresponding sampling data file found here. I have the following sample data variables:
- Primary sampling unit: the primary sampling unit within which the respondent was selected.
- Domain: Some countries use a different sample design in each of two or more parts of the country.
- Stratum: All except three of the 21 countries participating in ESS7 used some form of stratified sampling at the first stage of selection.
- Prob: the unscaled sample selection probability of the respondent (used for dweight).
- Design weight (dweight) to correct for varying probability of selection into sample due to sampling design
- Post-stratification weights (pspweight) to reduce potential sampling error and non-response.
- Population weight (pweight) to correct for country variation when comparing two or more countries.
To -svyset- for multilevel, the procedure on this Stata page looks intuitive, i.e. individuals within schools:
Code:
svyset school_id, weight(wt_school) || _n, weight(wt_student)
Code:
. svyset country, weight(pweight) || idno, weight(dweight)
Note: Stage 1 is sampled with replacement; further stages will be ignored for variance estimation.
pweight: <none>
VCE: linearized
Single unit: missing
Strata 1: <one>
SU 1: country
FPC 1: <zero>
Weight 1: pweight
Strata 2: <one>
SU 2: idno
FPC 2: <zero>
Weight 2: dweight
Code:
. svy: mepoisson dvrcdeva gndr || country:, irr
(running mepoisson on estimation sample)
Survey: Mixed-effects Poisson regression
Number of strata = 1 Number of obs = 39,895
Number of PSUs = 21 Population size = 34,913.011
Design df = 20
F( 0, 20) = .
Prob > F = .
------------------------------------------------------------------------------
| Linearized
dvrcdeva | IRR Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gndr | .9908461 . . . . .
_cons | 1.885248 . . . . .
-------------+----------------------------------------------------------------
country |
var(_cons)| 561179.7 . . .
------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline incidence rate (conditional on zero random effects).
(Note: This is a slightly edited repost from another thread as I thought this question was on a different topic than the original post. I have done some research on this topic and thought this post might be of relevance to other people with similar aims. Related post on weighting multilevel models with ESS here)
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