use "NHANES0708_all.dta"
svyset sdmvpsu [pw=wtmec2yr], strata(sdmvstra)
svy, subpop(if subpop2==1 & male==1): mean ridageyr,over(food_any)coeflegend
svy, subpop(if subpop2==1 & male==1): mean ridageyr,over(food_any)
test _b[c.ridageyr@0bn.food_any] =_b[c.ridageyr@1.food_any] pweight: wtmec2yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 16 Number of obs = 10,149
Number of PSUs = 32 Population size = 297,136,095
Subpop. no. obs = 677
Subpop. size = 12,094,827
Design df = 16
------------------------------------------------------------------------------
| Mean Legend
-------------+----------------------------------------------------------------
c.ridageyr@|
food_any |
0 | 9.704803 _b[c.ridageyr@0bn.food_any]
1 | 10.09739 _b[c.ridageyr@1.food_any]
------------------------------------------------------------------------------
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 16 Number of obs = 10,149
Number of PSUs = 32 Population size = 297,136,095
Subpop. no. obs = 677
Subpop. size = 12,094,827
Design df = 16
---------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
--------------------+------------------------------------------------
c.ridageyr@food_any |
0 | 9.704803 .5548951 8.528478 10.88113
1 | 10.09739 .2952791 9.47143 10.72336
---------------------------------------------------------------------
Adjusted Wald test
( 1) c.ridageyr@0bn.food_any - c.ridageyr@1.food_any = 0
F( 1, 16) = 0.55
Prob > F = 0.4707use "NHANES0708_all.dta"
svyset sdmvpsu, strata(sdmvstra)
svy,subpop(if subpop2==1 & male==1): mean ridageyr,over(food_any)
test _b[c.ridageyr@0bn.food_any] =_b[c.ridageyr@1.food_any] pweight: <none>
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running mean on estimation sample)
Survey: Mean estimation
Number of strata = 16 Number of obs = 10,149
Number of PSUs = 32 Population size = 10,149
Subpop. no. obs = 677
Subpop. size = 677
Design df = 16
---------------------------------------------------------------------
| Linearized
| Mean Std. Err. [95% Conf. Interval]
--------------------+------------------------------------------------
c.ridageyr@food_any |
0 | 9.455357 .4741716 8.450158 10.46056
1 | 9.576991 .2267987 9.096199 10.05778
---------------------------------------------------------------------
Adjusted Wald test
( 1) c.ridageyr@0bn.food_any - c.ridageyr@1.food_any = 0
F( 1, 16) = 0.08
Prob > F = 0.7806use "NHANES0708_all.dta"
svyset
ttest ridageyr if subpop2==1 & male==1,by(food_any)no survey characteristics are set
Two-sample t test with equal variances
------------------------------------------------------------------------------
Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval]
---------+--------------------------------------------------------------------
0 | 112 9.455357 .4252126 4.500027 8.61277 10.29794
1 | 565 9.576991 .1595503 3.792467 9.263606 9.890376
---------+--------------------------------------------------------------------
combined | 677 9.556869 .1504646 3.914973 9.261434 9.852303
---------+--------------------------------------------------------------------
diff | -.121634 .405212 -.9172616 .6739936
------------------------------------------------------------------------------
diff = mean(0) - mean(1) t = -0.3002
Ho: diff = 0 degrees of freedom = 675
Ha: diff < 0 Ha: diff != 0 Ha: diff > 0
Pr(T < t) = 0.3821 Pr(|T| > |t|) = 0.7641 Pr(T > t) = 0.6179use "NHANES0708_all.dta"
svyset sdmvpsu [pw=wtmec2yr], strata(sdmvstra)
svy, subpop(if subpop2==1 & male==1): ta race_eth food_any,col percent pweight: wtmec2yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running tabulate on estimation sample)
Number of strata = 16 Number of obs = 10,149
Number of PSUs = 32 Population size = 297,136,095
Subpop. no. obs = 677
Subpop. size = 12,094,827
Design df = 16
-------------------------------
RECODE of |
ridreth1 |
(Race/Eth |
nicity - | food_any
Recode) | 0 1 Total
----------+--------------------
White-NH | 70.88 42.17 49.12
Black-NH | 3.83 19.39 15.62
Mex Am | 12.43 24.55 21.62
Oth | 12.86 13.89 13.64
|
Total | 100 100 100
-------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(3) = 729.2291
Design-based F(2.34, 37.50) = 7.9025 P = 0.0008use "NHANES0708_all.dta"
svyset sdmvpsu, strata(sdmvstra)
svy, subpop(if subpop2==1 & male==1): ta race_eth food_any,col percent pweight: <none>
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
(running tabulate on estimation sample)
Number of strata = 16 Number of obs = 10,149
Number of PSUs = 32 Population size = 10,149
Subpop. no. obs = 677
Subpop. size = 677
Design df = 16
-------------------------------
RECODE of |
ridreth1 |
(Race/Eth |
nicity - | food_any
Recode) | 0 1 Total
----------+--------------------
White-NH | 48.21 24.6 28.51
Black-NH | 8.929 25.49 22.75
Mex Am | 25.89 32.92 31.76
Oth | 16.96 16.99 16.99
|
Total | 100 100 100
-------------------------------
Key: column percentage
Pearson:
Uncorrected chi2(3) = 464.7301
Design-based F(2.67, 42.75) = 5.4865 P = 0.0038use "NHANES0708_all.dta"
svyset
ta race_eth food_any if subpop2==1 & male==0,col chi2no survey characteristics are set
+-------------------+
| Key |
|-------------------|
| frequency |
| column percentage |
+-------------------+
RECODE of |
ridreth1 |
(Race/Ethn |
icity - | food_any
Recode) | 0 1 | Total
-----------+----------------------+----------
White-NH | 36 127 | 163
| 41.86 22.76 | 25.31
-----------+----------------------+----------
Black-NH | 21 153 | 174
| 24.42 27.42 | 27.02
-----------+----------------------+----------
Mex Am | 20 172 | 192
| 23.26 30.82 | 29.81
-----------+----------------------+----------
Oth | 9 106 | 115
| 10.47 19.00 | 17.86
-----------+----------------------+----------
Total | 86 558 | 644
| 100.00 100.00 | 100.00
Pearson chi2(3) = 15.4581 Pr = 0.001use "NHANES0708_all.dta"
svyset sdmvpsu [pw=wtmec2yr], strata(sdmvstra)
tabout race_eth food_any if subpop2==1 & male==1 using "Tabout.csv", c(col) f(2) svy replace
tabout food_any if subpop2==1 & male==1 using "Tabout.csv", c(mean ridageyr se) f(2) sort sum svy append pweight: wtmec2yr
VCE: linearized
Single unit: missing
Strata 1: sdmvstra
SU 1: sdmvpsu
FPC 1: <zero>
Survey results being calculated
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
.......
Table output written to: Tabout.csv
food_any
RECODE of ridreth1 (Race/Ethnicity - Recode) 0 1 Total
Prop. Prop. Prop.
White-NH 0.71 0.42 0.49
Black-NH 0.04 0.19 0.16
Mex Am 0.12 0.25 0.22
Oth 0.13 0.14 0.14
Total 1.00 1.00 1.00
Survey results being calculated
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..........
Table output written to: Tabout.csv
food_any
RECODE of ridreth1 (Race/Ethnicity - Recode) 0 1 Total
Prop. Prop. Prop.
White-NH 0.71 0.42 0.49
Black-NH 0.04 0.19 0.16
Mex Am 0.12 0.25 0.22
Oth 0.13 0.14 0.14
Total 1.00 1.00 1.00
food_any Mean SE
0 9.70 (0.55)
1 10.10 (0.30)
Total 10.00 (0.30)a: Counts are unweighted (practice using the tabout command to generate weighted column percentages with one line of code) b: Report mean ± SE (the “mean ± SD” label is incorrect in the paper) (practice using the tabout command to generate weighted means ± se for continuous variables) c: Discrepancies of counts of overweight & Obese There are minor discrepancies of the counts of overweight and obese, and for the percent with private insurance compared to the data reported in the Kohn article.
use "NHANES0708_all.dta"
tabout age_3cat race_eth bmicat food_any if subpop2==1 & male==1 using "T1.csv", c(freq col) f(0 1) replaceTable output written to: T1.csv
food_any
0 0 1 1 Total Total
No. % No. % No. %
RECODE of ridageyr (Age at Screening Adjudicated - Recode)
>
4 to 7 49 43.8 192 34.0 241 35.6
8 to 11 24 21.4 192 34.0 216 31.9
12 to 17 39 34.8 181 32.0 220 32.5
Total 112 100.0 565 100.0 677 100.0
RECODE of ridreth1 (Race/Ethnicity - Recode)
>
White-NH 54 48.2 139 24.6 193 28.5
Black-NH 10 8.9 144 25.5 154 22.7
Mex Am 29 25.9 186 32.9 215 31.8
Oth 19 17.0 96 17.0 115 17.0
Total 112 100.0 565 100.0 677 100.0
bmicat
Normal wt 75 67.0 357 63.2 432 63.8
Overweight 17 15.2 111 19.6 128 18.9
Obese 20 17.9 97 17.2 117 17.3
Total 112 100.0 565 100.0 677 100.0