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Setting up a survey design and getting SEs Looking at the effect of alternative designs on precision Subgroups and comparing groups Looking at regional rates Logistic regression Using replication methods back to top . svyset [pwei=weighta],psu(psu) strata(regstrat) pweight is weighta strata is regstrat psu is psu. svydes pweight: weighta Strata: regstrat PSU: psu #Obs per PSU Strata ---------------------------- regstrat #PSUs #Obs min mean max -------- -------- -------- -------- -------- -------- 101 2 48 23 24.0 25 102 2 45 21 22.5 24 103 2 58 18 29.0 40 104 2 65 26 32.5 39 105 2 52 24 26.0 28 lines cut out 717 2 38 16 19.0 22 718 2 57 24 28.5 33 719 2 51 24 25.5 27 -------- -------- -------- -------- -------- -------- 154 312 9047 12 29.0 43 . svyprop cigst1 ------------------------------------------------------------------------------ pweight: weighta Number of obs = 9047 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 9006.178 ------------------------------------------------------------------------------ Survey proportions estimation +-----------------------------------------------------------------------+ | cigst1 Obs Est. Prop. Std. Err. | |-----------------------------------------------------------------------| | Refused/Not answered 14 0.001534 0.000491 | | Dont know 16 0.002548 0.000716 | | schedule not obtained 3 0.000601 0.000371 | | not applicable 3 0.000230 0.000133 | | Never smoked cigarettes at all 3711 0.436668 0.006127 | |-----------------------------------------------------------------------| | Used to smoke cigarettes occasionally 269 0.030702 0.002425 | | Used to smoke cigarettes regularly 1895 0.196159 0.004592 | | Current cigarette smoker 3136 0.331559 0.005967 | +-----------------------------------------------------------------------+ . recode cigst1 (-9 -8 -6 =.) (-1 1 2 3=0) (4=1),gen(smoker) . svymean smoker,deff deft ci Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. [95% Conf. Interval] Deff ---------+-------------------------------------------------------------------- smoker | .3331194 .0060102 .3212486 .3449902 1.465561 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Mean | Deft ---------+-------------------------------------------------------------------- smoker | 1.210604 ------------------------------------------------------------------------------ back to top The next set of results look at results on design effects etc of ignoring design features first just weights . svyset, clear(all) no variables are set . svyset [pwei=weighta] pweight is weighta . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata:Number of strata = 1 PSU: smoker | .3331194 .0057008 1.318523 1.14827 ------------------------------------------------------------------------------ DE is just a bit >1 from weighting - now add strata . svyset, clear(all) no variables are set . svyset [pwei=weighta],strata(regstrat) pweight is weighta strata is regstrat . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU:Number of PSUs = 9014 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- Number of PSUs = 9014 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- smoker | .3331194 .0056322 1.286988 1.134455 ------------------------------------------------------------------------------ Improves things only a little -now psus no strata . svyset, clear(all) no variables are set . svyset [pwei=weighta],psu(psu) pweight is weighta psu is psu . svymean smoker,deff deft Survey mean estimation pweight: weighta Number of obs = 9014 Strata:Number of strata = 1 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. Deff Deft ---------+-------------------------------------------------------------------- smoker | .3331194 .0074961 2.279774 1.509892 ------------------------------------------------------------------------------ This shows that it is the PSUs that are the main reason for reduced precision . svyset, clear(all) no variables are set back to top Now the effect of subgroups and comparisons between groups first go back to the original design . svyset [pwei=weighta],strata(regstrat) psu(psu) pweight is weighta strata is regstrat psu is psu . > now looking at rates by sex . svymean smoker, by(sex) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | male | .3419507 .0084952 .3251718 .3587296 1.419651 female | .3245964 .0078806 .3090315 .3401614 1.29928 ------------------------------------------------------------------------------ to get a test of differrences by sex use lincom for linear combinations . lincom [smoker]male-[smoker]female ( 1) [smoker]male - [smoker]female = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0173543 .0111258 1.56 0.121 -.0046203 .0393288 ------------------------------------------------------------------------------ > and by adults in the household . svymean smoker, by(nofad) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | nofad==1 | .4408193 .0099747 .4211183 .4605202 .6521785 nofad==2 | .3189418 .0073425 .3044398 .3334438 1.219789 nofad==3 | .2887937 .0145437 .2600685 .3175188 1.607311 nofad==4 | .2894893 .0283638 .2334682 .3455104 2.801671 nofad==5 | .3479849 .0740851 .2016601 .4943097 3.931923 nofad==6 | 0 0 0 0 . nofad==7 | 0 0 0 0 . nofad==8 | .617777 .3339362 -.0417778 1.277332 6.100542 nofad==9 | 0 0 0 0 . ------------------------------------------------------------------------------ . /*-----and compare nofad=1 with nofad=2-----------------*/ . lincom [smoker]1-[smoker]2 ( 1) [smoker]1 - [smoker]2 = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .1218775 .012969 9.40 0.000 .0962625 .1474925 ------------------------------------------------------------------------------ back to top . /*------------------------------------------------- > smoking rates by region or health board are also easily calculated > and lincom can give the comparisons between any pair > or other combination > > -------------------------------------------------------*/ . svymean smoker, by(region) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | Highland | .3278274 .0250704 .2783111 .3773436 1.375797 Grampian | .3267013 .0160343 .295032 .3583705 1.900358 Lothian_ | .3213779 .0114178 .2988266 .3439292 1.198679 Borders, | .2893518 .0228964 .2441293 .3345743 1.128549 Glagow | .3633258 .0164955 .3307457 .395906 1.845421 Lanarksh | .3425938 .0131008 .3167184 .3684691 1.256116 Forth_Va | .3273929 .0154412 .296895 .3578907 1.342229 ------------------------------------------------------------------------------ . svymean smoker, by(hboard) Survey mean estimation pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 ------------------------------------------------------------------------------ Mean Subpop. | Estimate Std. Err. [95% Conf. Interval] Deff ---------------+-------------------------------------------------------------- smoker | Ayreshir | .3405227 .0249012 .2913406 .3897047 2.091303 Borders | .2751782 .0339687 .2080869 .3422696 1.216756 Argyll_& | .3377994 .0226662 .2930315 .3825673 1.405142 Fife | .3514359 .0179934 .3158974 .3869745 1.048564 Greater_ | .3633258 .0164955 .3307457 .395906 1.845421 Highland | .3594827 .0311305 .2979971 .4209684 1.563275 Lanarksh | .3443544 .0187207 .3073794 .3813295 1.382976 Grampian | .2982887 .0189685 .2608242 .3357531 1.442587 Orkney | .2323456 .0107839 .2110463 .2536448 .0225086 Lothian | .3038667 .0152057 .2738341 .3338993 1.385 Tayside | .3570112 .024537 .3085483 .4054741 2.063279 Forth_Va | .317252 .0228504 .2721205 .3623836 1.513442 Western_ | .2505246 .0263358 .198509 .3025402 .1420666 Dumfries | .3021828 .0269545 .2489452 .3554204 .8004963 Shetland | .1831689 .0670901 .0506597 .3156781 1.141379 ------------------------------------------------------------------------------ . . lincom [smoker]Fife-[smoker]Lothian ( 1) [smoker]Fife - [smoker]Lothian = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0475692 .0237606 2.00 0.047 .0006399 .0944985 ------------------------------------------------------------------------------ . lincom [smoker]Lanarksh-[smoker]Ayreshir ( 1) - [smoker]Ayreshir + [smoker]Lanarksh = 0 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- (1) | .0038318 .0349296 0.11 0.913 -.0651573 .0728209 ------------------------------------------------------------------------------ back to top . . /*--------- spelling mistake was in original file-----*/ . /*----------------------------------------------------- > now logistic regressions to predict smoking > > To use categorical variables you must first generate a set of dummy variables > here for number of adults > --------------------------------------------------*/ . tabulate nofad,generate(nofad) Number of | adults. | Freq. Percent Cum. ------------+----------------------------------- 1 | 3,046 33.67 33.67 2 | 4,613 50.99 84.66 3 | 992 10.96 95.62 4 | 330 3.65 99.27 5 | 56 0.62 99.89 6 | 6 0.07 99.96 7 | 1 0.01 99.97 8 | 2 0.02 99.99 9 | 1 0.01 100.00 ------------+----------------------------------- Total | 9,047 100.00 . /*---------------------------------------------- > check the data set to see the new variables > as there are so few households of more than 5 > it seems sensible to group them together > and then to carry out the regression > ---------------------------------------------------*/ . replace nofad5=1 if nofad>5 (10 real changes made) . /*---regressions include the comparisons with nofad1 only--------*/ . svylogit smoker nofad2 nofad3 nofad4 Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 3, 156) = 25.41 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.4625912 .0598125 -7.73 0.000 -.5807264 -.3444561 nofad3 | -.6052021 .0829158 -7.30 0.000 -.7689683 -.4414358 nofad4 | -.6018177 .147868 -4.07 0.000 -.8938706 -.3097647 _cons | -.296048 .0465228 -6.36 0.000 -.3879349 -.2041611 ------------------------------------------------------------------------------ . /*------------ we can compare with simple logistic regression--------- > --------------use coef to get comaparable results to the svy command----*/ . logistic smoker nofad2 nofad3 nofad4,coef Logistic regression Number of obs = 9014 LR chi2(3) = 143.44 Prob > chi2 = 0.0000 Log likelihood = -5752.579 Pseudo R2 = 0.0123 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.5172813 .0482623 -10.72 0.000 -.6118737 -.4226889 nofad3 | -.6473839 .0795895 -8.13 0.000 -.8033764 -.4913914 nofad4 | -.6380871 .1279033 -4.99 0.000 -.8887729 -.3874012 _cons | -.2803519 .0362628 -7.73 0.000 -.3514257 -.2092781 ------------------------------------------------------------------------------ . /*-------------- and we can get more complicated models > looking at joint effect of age group sex > and number of adults > Test commands can be used to check if variables are significant in > the larger models > --------------------------------------------------------------*/ . tabulate hboard,generate(hboard) Health Board | Freq. Percent Cum. --------------------+----------------------------------- Ayreshire & Arran | 744 8.22 8.22 Borders | 388 4.29 12.51 Argyll & Clyde | 614 6.79 19.30 Fife | 662 7.32 26.62 Greater Glasgow | 1,294 14.30 40.92 Highland | 681 7.53 48.45 Lanarkshire | 871 9.63 58.07 Grampian | 726 8.02 66.10 Orkney | 63 0.70 66.80 Lothian | 1,174 12.98 79.77 Tayside | 725 8.01 87.79 Forth Valley | 509 5.63 93.41 Western Isles | 98 1.08 94.50 Dumfries & Galloway | 438 4.84 99.34 Shetland | 60 0.66 100.00 --------------------+----------------------------------- Total | 9,047 100.00 . tabulate ageg,generate(ageg) ageg | Freq. Percent Cum. ------------+----------------------------------- 16-19 | 391 4.32 4.32 25-29 | 536 5.92 10.25 35-39 | 765 8.46 18.70 45-49 | 973 10.75 29.46 55-59 | 984 10.88 40.33 65-69 | 852 9.42 49.75 70-74 | 759 8.39 58.14 60-64 | 831 9.19 67.33 50-54 | 742 8.20 75.53 40-44 | 750 8.29 83.82 30-34 | 760 8.40 92.22 20-24 | 704 7.78 100.00 ------------+----------------------------------- Total | 9,047 100.00 . tabulate sex,generate(sex) Sex of | respondent | from | household | grid. O | Freq. Percent Cum. ------------+----------------------------------- male | 3,941 43.56 43.56 female | 5,106 56.44 100.00 ------------+----------------------------------- Total | 9,047 100.00 . svylogit smoker nofad2 nofad3 nofad4 Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 3, 156) = 25.41 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.4625912 .0598125 -7.73 0.000 -.5807264 -.3444561 nofad3 | -.6052021 .0829158 -7.30 0.000 -.7689683 -.4414358 nofad4 | -.6018177 .147868 -4.07 0.000 -.8938706 -.3097647 _cons | -.296048 .0465228 -6.36 0.000 -.3879349 -.2041611 ------------------------------------------------------------------------------ . svylogit smoker nofad2 nofad3 nofad4 sex2 ageg2-ageg12 hboard2-hboard15 Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 29, 130) = 11.94 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.5612387 .0609933 -9.20 0.000 -.681706 -.4407714 nofad3 | -.7485357 .0909213 -8.23 0.000 -.9281135 -.5689578 nofad4 | -.7860213 .1571321 -5.00 0.000 -1.096372 -.475671 sex2 | -.1193292 .0521901 -2.29 0.024 -.2224095 -.0162489 ageg2 | .4888972 .1821246 2.68 0.008 .1291843 .8486101 ageg3 | .2063644 .1559656 1.32 0.188 -.1016821 .5144109 ageg4 | .3205004 .1600491 2.00 0.047 .0043888 .6366121 ageg5 | .1117259 .1457159 0.77 0.444 -.1760764 .3995282 ageg6 | .2468631 .1576949 1.57 0.119 -.064599 .5583251 ageg7 | .1650847 .1686705 0.98 0.329 -.1680551 .4982244 ageg8 | .1918504 .1501647 1.28 0.203 -.1047388 .4884395 ageg9 | .1454189 .1645114 0.88 0.378 -.1795063 .4703441 ageg10 | -.1560555 .1598613 -0.98 0.330 -.4717963 .1596854 ageg11 | -.4393657 .1757703 -2.50 0.013 -.7865283 -.0922032 ageg12 | -.7704922 .1644425 -4.69 0.000 -1.095281 -.4457032 hboard2 | -.2949776 .203661 -1.45 0.149 -.6972269 .1072717 hboard3 | -.0308084 .145793 -0.21 0.833 -.318763 .2571462 hboard4 | .017459 .1351148 0.13 0.897 -.2494052 .2843233 hboard5 | .0521162 .1321553 0.39 0.694 -.2089026 .3131351 hboard6 | .0988347 .1723596 0.57 0.567 -.2415914 .4392609 hboard7 | .0257908 .1519292 0.17 0.865 -.2742833 .3258648 hboard8 | -.2064544 .1482992 -1.39 0.166 -.4993589 .0864502 hboard9 | -.5131615 .1361878 -3.77 0.000 -.7821449 -.2441781 hboard10 | -.2055899 .1291238 -1.59 0.113 -.4606214 .0494415 hboard11 | .0477432 .1538141 0.31 0.757 -.2560537 .3515401 hboard12 | -.1433606 .1576678 -0.91 0.365 -.4547691 .1680478 hboard13 | -.4630294 .1779328 -2.60 0.010 -.814463 -.1115958 hboard14 | -.1637861 .1662252 -0.99 0.326 -.4920962 .164524 hboard15 | -.8051732 .4804435 -1.68 0.096 -1.754093 .1437469 _cons | -.196425 .1790967 -1.10 0.274 -.5501576 .1573076 ------------------------------------------------------------------------------ . test sex2 Adjusted Wald test ( 1) sex2 = 0 F( 1, 158) = 5.23 Prob > F = 0.0236 . test ageg2 ageg3 ageg4 ageg5 ageg6 ageg7 ageg8 ageg9 ageg10 ageg11 ageg12 Adjusted Wald test ( 1) ageg2 = 0 ( 2) ageg3 = 0 ( 3) ageg4 = 0 ( 4) ageg5 = 0 ( 5) ageg6 = 0 ( 6) ageg7 = 0 ( 7) ageg8 = 0 ( 8) ageg9 = 0 ( 9) ageg10 = 0 (10) ageg11 = 0 (11) ageg12 = 0 F( 11, 148) = 13.25 Prob > F = 0.0000 . /*----------------------------------------------------------------- > get dummies for the age sex interaction > --------------------------------------------------------------------*/ . generate ageg2s=ageg2*(sex==1) . generate ageg3s=ageg3*(sex==1) . generate ageg4s=ageg4*(sex==1) . generate ageg5s=ageg5*(sex==1) . generate ageg6s=ageg6*(sex==1) . generate ageg7s=ageg7*(sex==1) . generate ageg8s=ageg8*(sex==1) . generate ageg9s=ageg9*(sex==1) . generate ageg10s=ageg10*(sex==1) . generate ageg11s=ageg11*(sex==1) . generate ageg12s=ageg12*(sex==1) . svylogit smoker nofad2 nofad3 nofad4 sex2 ageg2-ageg12 hboard2-hboard15 ageg2s-ageg12s Survey logistic regression pweight: weighta Number of obs = 9014 Strata: regstrat Number of strata = 154 PSU: psu Number of PSUs = 312 Population size = 8964.0037 F( 40, 119) = 7.71 Prob > F = 0.0000 ------------------------------------------------------------------------------ smoker | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- nofad2 | -.5649544 .0608856 -9.28 0.000 -.6852091 -.4446997 nofad3 | -.7536616 .0926128 -8.14 0.000 -.9365805 -.5707427 nofad4 | -.8098296 .1577345 -5.13 0.000 -1.12137 -.4982894 sex2 | .2744983 .2710147 1.01 0.313 -.2607807 .8097773 ageg2 | .0620744 .2462163 0.25 0.801 -.4242254 .5483743 ageg3 | .0229399 .2295454 0.10 0.921 -.4304334 .4763131 ageg4 | -.0247639 .2006926 -0.12 0.902 -.4211503 .3716225 ageg5 | -.1542414 .1982643 -0.78 0.438 -.5458315 .2373488 ageg6 | .0865896 .2108846 0.41 0.682 -.3299268 .5031061 ageg7 | -.0092496 .224278 -0.04 0.967 -.4522192 .4337201 ageg8 | .0533689 .1981527 0.27 0.788 -.3380009 .4447388 ageg9 | .1510162 .2365119 0.64 0.524 -.3161166 .618149 ageg10 | -.5590369 .2192275 -2.55 0.012 -.9920313 -.1260425 ageg11 | -.5204729 .2332741 -2.23 0.027 -.9812107 -.0597351 ageg12 | -.8953448 .2270752 -3.94 0.000 -1.343839 -.4468504 hboard2 | -.3028365 .207801 -1.46 0.147 -.7132627 .1075896 hboard3 | -.0337248 .1478331 -0.23 0.820 -.3257087 .2582592 hboard4 | .0032226 .1385716 0.02 0.981 -.2704691 .2769143 hboard5 | .0453948 .1320654 0.34 0.732 -.2154465 .306236 hboard6 | .0938063 .1739568 0.54 0.590 -.2497744 .437387 hboard7 | .0224807 .1529716 0.15 0.883 -.2796522 .3246136 hboard8 | -.2122447 .1511458 -1.40 0.162 -.5107715 .0862821 hboard9 | -.5275344 .1574306 -3.35 0.001 -.8384743 -.2165945 hboard10 | -.2124226 .1302862 -1.63 0.105 -.4697497 .0449046 hboard11 | .0445227 .1562242 0.28 0.776 -.2640345 .35308 hboard12 | -.1588901 .1584109 -1.00 0.317 -.4717662 .153986 hboard13 | -.4628882 .1811377 -2.56 0.012 -.8206519 -.1051246 hboard14 | -.1826604 .1682906 -1.09 0.279 -.5150499 .1497291 hboard15 | -.8062307 .4707845 -1.71 0.089 -1.736073 .1236121 ageg2s | .824665 .3715844 2.22 0.028 .0907517 1.558578 ageg3s | .3504848 .3230779 1.08 0.280 -.2876237 .9885933 ageg4s | .6673566 .2912065 2.29 0.023 .092197 1.242516 ageg5s | .5117199 .2978448 1.72 0.088 -.076551 1.099991 ageg6s | .3084944 .3122679 0.99 0.325 -.3082634 .9252521 ageg7s | .3364531 .3147512 1.07 0.287 -.2852094 .9581156 ageg8s | .2614396 .3404926 0.77 0.444 -.4110647 .9339438 ageg9s | -.0546145 .3289639 -0.17 0.868 -.7043484 .5951195 ageg10s | .7881987 .3413227 2.31 0.022 .114055 1.462342 ageg11s | .1063126 .3237929 0.33 0.743 -.5332082 .7458333 ageg12s | .1880666 .3350127 0.56 0.575 -.4736143 .8497476 _cons | -.3746603 .2330058 -1.61 0.110 -.8348683 .0855476 ------------------------------------------------------------------------------ . test ageg2s ageg3s ageg4s ageg5s ageg6s ageg7s ageg8s ageg9s ageg10s ageg11s ageg12s Adjusted Wald test ( 1) ageg2s = 0 ( 2) ageg3s = 0 ( 3) ageg4s = 0 ( 4) ageg5s = 0 ( 5) ageg6s = 0 ( 6) ageg7s = 0 ( 7) ageg8s = 0 ( 8) ageg9s = 0 ( 9) ageg10s = 0 (10) ageg11s = 0 (11) ageg12s = 0 F( 11, 148) = 2.02 Prob > F = 0.0303 back to top . various steps need to be taken to set up for replication methods memory needs to be increased and total for the population need to be attached to each record details are in the code file output from these are not shown Since the data are alreadypost-stratified we get the totals by adding the weights up from the data file, since they have already been ste to match the population you now have a file with agesex and region totals now make a set of jacknife weights for this survey . survwgt create jkn, psu(psu) weight(weight) strata(regstrat) Generating replicate weights...................................................................................... > ................................................................................................................ > ................................................................................................................ > .. Created weights and set svr values: meth jkn pw weighta rw jkn_1 jkn_2 jkn_3 jkn_4 jkn_5 jkn_6 jkn_7 jkn_8 jkn_9 jkn_10 jkn_11 jkn_12 jkn_13 jkn_14 jkn_15 jkn_16 jkn_17 jkn_18 jkn_19 jkn_20 jkn_21 jkn_22 jkn_23 jkn_24 jkn_25 jkn_26 jkn_27 jkn_28 jkn_29 jkn_30 jkn_31 jkn_32 jkn_33 jkn_34 jkn_35 jkn_36 jkn_37 jkn_38 jkn_39 jkn_40 jkn_41 jkn_42 jkn_43 jkn_44 jkn_45 jkn_46 lines missed jkn_276 jkn_277 jkn_278 jkn_279 jkn_280 jkn_281 jkn_282 jkn_283 jkn_284 jkn_285 jkn_286 jkn_287 jkn_288 jkn_289 jkn_290 jkn_291 jkn_292 jkn_293 jkn_294 jkn_295 jkn_296 jkn_297 jkn_298 jkn_299 jkn_300 jkn_301 jkn_302 jkn_303 jkn_304 jkn_305 jkn_306 jkn_307 jkn_308 jkn_309 jkn_310 jkn_311 jkn_312 dof 158 now use the survey replication commands first raking the original weight and all the jackknife weights This which does not change the weights for the main weight here, since they totals match already but it does change the kacknife weights becasue they don't match . survwgt rake [all] , by(agesex region) totvars( asext rtot) replace SVR settings updated: pw weighta rw jkn_1 jkn_2 jkn_3 jkn_4 jkn_5 jkn_6 jkn_7 jkn_8 jkn_9 jkn_10 jkn_11 jkn_12 jkn_13 jkn_14 jkn_15 jkn_16 jkn_17 jkn_18 jkn_19 jkn_20 jkn_21 jkn_22 jkn_23 jkn_24 jkn_25 jkn_26 jkn_27 jkn_28 jkn_29 jkn_30 jkn_31 lines missed out jkn_275 jkn_276 jkn_277 jkn_278 jkn_279 jkn_280 jkn_281 jkn_282 jkn_283 jkn_284 jkn_285 jkn_286 jkn_287 jkn_288 jkn_289 jkn_290 jkn_291 jkn_292 jkn_293 jkn_294 jkn_295 jkn_296 jkn_297 jkn_298 jkn_299 jkn_300 jkn_301 jkn_302 jkn_303 jkn_304 jkn_305 jkn_306 jkn_307 jkn_308 jkn_309 jkn_310 jkn_311 jkn_312 . save ex3reps,replace file ex3reps.dta saved > now use the replicate mean command to get the mean and design effect > for smokers using a jacknife method . svrmean smoker Survey mean estimation, replication (jkn) variance method Analysis weight: weighta Number of obs = 9014 Replicate weights: jkn_1... Population size = 8964.0037 Number of replicates: 312 Degrees of freedom = 158 ------------------------------------------------------------------------------ Mean | Estimate Std. Err. [95% Conf. Interval] Deff ---------+-------------------------------------------------------------------- smoker | .3331194 .0059549 .321358 .3448808 1.438681 ------------------------------------------------------------------------------ After all that trouble we find it makes almost no difference. The original design effect was 1.465