HI all, I'm analyzing survey data. I have a series of proportions and would like to produce standard errors and 90% CIs around the proportions. I've accounted for the survey design and incorporated replicate weights. Due to the sample size, this analysis produces some categories with very small numbers of people. As others have pointed out, and supported by the below code, proportion and tabulate seem to be producing the same proportions and standard errors, but the 90% CIs differ. (Note: in the below code, the category subpop_7 in the proportion results should match the results from the tabulate command). My questions:

1) I am leaning toward using the tabulate results for the CIs, as some of the CIs using the "proportion" option are negative. Thoughts?

2) Are there other ways of calculating the 90% CIs in Stata for survey proportions that I should consider here?

3) Are there any good applied research studies with good examples of how to present the proportions and SEs or 90% CIs? (tables or graphs) - maybe not a Stata question.

thanks in advance for any advice!


Code:

. svyset [pw = WTSURVY], jkrw(RW0001- RW0320, multiplier(0.05)) vce(jack) mse

      pweight: WTSURVY
          VCE: jackknife
          MSE: on
    jkrweight: RW0001 .. RW0320
  Single unit: missing
     Strata 1: <one>
         SU 1: <observations>
        FPC 1: <zero>



. svy: proportion RACETHM_n, over(career_stage_rev2 DGRDG_n) level(90)
(running proportion on estimation sample)

Jackknife replications (320)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200
..................................................   250
..................................................   300
....................

Survey: Proportion estimation

Number of strata =       1        Number of obs   =      1,311
                                  Population size = 252,142.35
                                  Replications    =        320
                                  Design df       =        319

   AsianNHOPI: RACETHM_n = AsianNHOPI
         AIAN: RACETHM_n = AIAN
        Black: RACETHM_n = Black
     Hispanic: RACETHM_n = Hispanic
        White: RACETHM_n = White
           MR: RACETHM_n = MR

         Over: career_stage_rev2 DGRDG_n
    _subpop_1: 20 or more years Bachelors
    _subpop_2: 20 or more years Masters
    _subpop_3: 20 or more years Doctorate
    _subpop_4: 20 or more years Professional
    _subpop_5: Less than 20 yrs Bachelors
    _subpop_6: Less than 20 yrs Masters
    _subpop_7: Less than 20 yrs Doctorate
    _subpop_8: Less than 20 yrs Professional

--------------------------------------------------------------
             |              Jknife *N             ormal
        Over | Proportion   Std. Err.     [90% Conf. Interval]
-------------+------------------------------------------------
AsianNHOPI   |
   _subpop_1 |   .0232649   .0103291      .0062255    .0403043
   _subpop_2 |   .0101458   .0081955     -.0033739    .0236655
   _subpop_3 |   .0861882   .0234436      .0475145    .1248618
   _subpop_4 |          0  (no observations)
   _subpop_5 |   .1010706    .025582      .0588694    .1432719
   _subpop_6 |   .1334251   .0323168      .0801139    .1867364
   _subpop_7 |   .2483284    .043813      .1760524    .3206043
   _subpop_8 |          0  (no observations)
-------------+------------------------------------------------
AIAN         |
   _subpop_1 |          0  (no observations)
   _subpop_2 |    .022717   .0171829     -.0056286    .0510626
   _subpop_3 |          0  (no observations)
   _subpop_4 |          0  (no observations)
   _subpop_5 |    .000104    .000122     -.0000973    .0003053
   _subpop_6 |   .0080136    .005543     -.0011304    .0171576
   _subpop_7 |          0  (no observations)
   _subpop_8 |          0  (no observations)
-------------+------------------------------------------------
Black        |
   _subpop_1 |   .0325514   .0203369     -.0009974    .0661001
   _subpop_2 |   .0865779   .0572381     -.0078446    .1810005
   _subpop_3 |   .0072528   .0054652     -.0017628    .0162684
   _subpop_4 |          0  (no observations)
   _subpop_5 |   .0464535   .0292895     -.0018638    .0947708
   _subpop_6 |   .0848761   .0471426      .0071076    .1626445
   _subpop_7 |   .0030085   .0018134       .000017        .006
   _subpop_8 |          0  (no observations)
-------------+------------------------------------------------
Hispanic     |
   _subpop_1 |   .0366649   .0248132     -.0042681    .0775978
   _subpop_2 |   .0493453   .0213093      .0141927     .084498
   _subpop_3 |   .0232171   .0143399     -.0004386    .0468728
   _subpop_4 |          0  (no observations)
   _subpop_5 |   .0834066   .0350203      .0256355    .1411777
   _subpop_6 |   .0727584   .0242182       .032807    .1127099
   _subpop_7 |   .0743311   .0250366      .0330296    .1156325
   _subpop_8 |   .2790089   .2699777     -.1663584    .7243761
-------------+------------------------------------------------
White        |
   _subpop_1 |   .8807481    .043279       .809353    .9521431
   _subpop_2 |   .8079233   .0656598       .699608    .9162386
   _subpop_3 |    .880132   .0284381      .8332192    .9270448
   _subpop_4 |          1          .             .           .
   _subpop_5 |   .7615289   .0511107      .6772145    .8458433
   _subpop_6 |   .6686341   .0495443      .5869037    .7503645
   _subpop_7 |   .6694451   .0474141      .5912287    .7476614
   _subpop_8 |   .2771716   .2752663     -.1769198     .731263
-------------+------------------------------------------------
MR           |
   _subpop_1 |   .0267708   .0221536     -.0097747    .0633164
   _subpop_2 |   .0232907   .0153762     -.0020746     .048656
   _subpop_3 |   .0032099   .0024432     -.0008206    .0072404
   _subpop_4 |          0  (no observations)
   _subpop_5 |   .0074364   .0028953      .0026601    .0122126
   _subpop_6 |   .0322927   .0186794      .0014783     .063107
   _subpop_7 |    .004887    .003178     -.0003555    .0101295
   _subpop_8 |   .4438195   .2456526      .0385802    .8490589
--------------------------------------------------------------

. 
. svy, subpop(if career_stage_rev2==2 & DGRDG_n==3): tabulate RACETHM_n, se ci level(90)
(running tabulate on estimation sample)

Number of strata   =         1                  Number of obs     =      1,311
                                                Population size   = 252,142.35
                                                Subpop. no. obs   =        241
                                                Subpop. size      =  43,459.37
                                                Replications      =        320
                                                Design df         =        319

----------------------------------------------------------
RACETHM_n | proportion          se          lb          ub
----------+-----------------------------------------------
 AsianNHO |      .2483       .0438       .1832       .3273
     AIAN |          0           0                        
    Black |       .003       .0018       .0011       .0081
 Hispanic |      .0743        .025       .0422       .1277
    White |      .6694       .0474       .5872       .7425
       MR |      .0049       .0032       .0017       .0142
          | 
    Total |          1                                    
----------------------------------------------------------
  Key:  proportion  =  cell proportion
        se          =  jackknife standard error of cell proportion
        lb          =  lower 90% confidence bound for cell proportion
        ub          =  upper 90% confidence bound for cell proportion

  Table contains a zero in the marginals.
  Statistics cannot be computed.