HI, I have a general question about the difference in 2 commands. I have multilevel survey data and an ordered 3 level dependent variable (outcome). I'm wondering what the difference is in the commands between a)survey setting the data and running meologit as a svy command vs. b)running meologit and adding in sampling weights. Does anyone have thoughts on that? (Note: I didn't use dataex because I'm not worried about replicating this or how it is running, just whether there are differences between these 2 commands).

Many thanks to any who might reply!
--Ann

Examples below, outcome=ckd_3cat and independent var=stunting
example a:
svydescribe

Survey: Describing stage 1 sampling units

pweight: w_ind_norm
VCE: linearized
Single unit: missing
Strata 1: site
SU 1: hogar
FPC 1: <zero>
. svy: meologit ckdu_3cat stunting
(running meologit on estimation sample)

Survey: Ordered logistic regression

Number of strata = 2 Number of obs = 773
Number of PSUs = 314 Population size = 814.402204
Design df = 312
F( 1, 312) = 2.55
Prob > F = 0.1115

------------------------------------------------------------------------------
| Linearized
ckdu_3cat | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
stunting | .4876653 .3055801 1.60 0.112 -.113593 1.088924
-------------+----------------------------------------------------------------
/cut1 | 2.569918 .207242 2.162149 2.977686
/cut2 | 6.545487 .6893908 5.189044 7.90193
------------------------------------------------------------------------------


example B:
meologit ckdu_3cat stunting [pweight = w_ind_norm] || hogar:

Fitting fixed-effects model:

Iteration 0: log likelihood = -241.89419
Iteration 1: log likelihood = -237.86994
Iteration 2: log likelihood = -237.30416
Iteration 3: log likelihood = -237.3033
Iteration 4: log likelihood = -237.3033

Refining starting values:

Grid node 0: log likelihood = -232.26489

Fitting full model:

Iteration 0: log pseudolikelihood = -232.26489
Iteration 1: log pseudolikelihood = -227.83392
Iteration 2: log pseudolikelihood = -227.57108
Iteration 3: log pseudolikelihood = -227.56916
Iteration 4: log pseudolikelihood = -227.56916

Mixed-effects ologit regression Number of obs = 773
Group variable: hogar Number of groups = 313

Obs per group:
min = 1
avg = 2.5
max = 10

Integration method: mvaghermite Integration pts. = 7

Wald chi2(1) = 2.57
Log pseudolikelihood = -227.56916 Prob > chi2 = 0.1091
(Std. Err. adjusted for 313 clusters in hogar)
------------------------------------------------------------------------------
| Robust
ckdu_3cat | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
stunting | .5356627 .3342881 1.60 0.109 -.1195299 1.190855
-------------+----------------------------------------------------------------
/cut1 | 3.25844 .3152659 2.64053 3.87635
/cut2 | 7.401028 .7907553 5.851176 8.95088
-------------+----------------------------------------------------------------
hogar |
var(_cons)| 1.625323 .5789674 .808588 3.267021
------------------------------------------------------------------------------