Dear experts
Regarding statistics to population survey, could you please tell me which one of the syntax using for bivariate analysis [chi square] and what does different the meaning of each syntax below :
How to make odds ratio for cross-sectional design survey? Should I make syntax for prevalence ratio or may I take directly odds ratio in the syntax below?
Based on the table above [chi square and binary logistic].
Where the sex variable which assumptions male is given code = 0 and female is given code = 1.
Malaria prevalence differs by sex Males are more likely to have malaria than females (1.85% males versus 1.62% females, P = 0.000). Based on odds ratio (OR) female have the chances of getting malaria 0.85% or 0.85 times than male (as categorical reference)
How do I interpret an odds ratio less than 1 in a logistic regression?
May I will be written male with a chance of 1 / 0.85 times or 1.2 times to get malaria compared than female as well?
or
The odds of malaria in male decreased by (1 - 0.85 ) 15% compared those in a female. Whatever on the dependent variable decreases. For each unit increase, it decreases by a multiple of (1 - OR )
Thank you in advance for your reply
Sincerely yours,
Hamzah
Regarding statistics to population survey, could you please tell me which one of the syntax using for bivariate analysis [chi square] and what does different the meaning of each syntax below :
Code:
1.
svy: tabulate sex malaria
and output here :
Number of strata = 1 Number of obs = 259,885
Number of PSUs = 4,418 Population size = 30,152,652
Design df = 4,417
-------------------------------
gender of |
responden | malaria
ts | no yes Total
----------+--------------------
male | .4744 .0185 .4929
female | .4909 .0162 .5071
|
Total | .9653 .0347 1
-------------------------------
Key: cell proportion
Pearson:
Uncorrected chi2(1) = 58.3020
Design-based F(1, 4417) = 49.6352 P = 0.0000
Code:
2.
. svy: tabulate sex malaria, row
and output here :
(running tabulate on estimation sample)
Number of strata = 1 Number of obs = 259,885
Number of PSUs = 4,418 Population size = 30,152,652
Design df = 4,417
-------------------------------
gender of |
responden | malaria
ts | no yes Total
----------+--------------------
male | .9625 .0375 1
female | .968 .032 1
|
Total | .9653 .0347 1
-------------------------------
Key: row proportion
Pearson:
Uncorrected chi2(1) = 58.3020
Design-based F(1, 4417) = 49.6352 P = 0.0000
Code:
3.
. svy linearized : tabulate sex malaria, obs row percent ci
and output here :
(running tabulate on estimation sample)
Number of strata = 1 Number of obs = 259,885
Number of PSUs = 4,418 Population size = 30,152,652
Design df = 4,417
-------------------------------------------------------
gender of |
responden | malaria
ts | no yes Total
----------+--------------------------------------------
male | 96.25 3.746 100
| [96.01,96.48] [3.518,3.987]
| 1.2e+05 5595 1.3e+05
|
female | 96.8 3.198 100
| [96.57,97.02] [2.979,3.431]
| 1.3e+05 4971 1.3e+05
|
Total | 96.53 3.468 100
| [96.31,96.74] [3.257,3.692]
| 2.5e+05 1.1e+04 2.6e+05
-------------------------------------------------------
Key: row percentage
[95% confidence interval for row percentage]
number of observations
Pearson:
Uncorrected chi2(1) = 58.3020
Design-based F(1, 4417) = 49.6352 P = 0.0000
Code:
4.
. svy linearized : logistic sex malaria
and output here :
(running logistic on estimation sample)
Survey: Logistic regression
Number of strata = 1 Number of obs = 259,885
Number of PSUs = 4,418 Population size = 30,152,652
Design df = 4,417
F( 1, 4417) = 49.54
Prob > F = 0.0000
------------------------------------------------------------------------------
| Linearized
sex | Odds Ratio Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
malaria | .8488294 .0197667 -7.04 0.000 .8109481 .8884803
_cons | 1.034818 .0042681 8.30 0.000 1.026484 1.043219
------------------------------------------------------------------------------
Where the sex variable which assumptions male is given code = 0 and female is given code = 1.
Malaria prevalence differs by sex Males are more likely to have malaria than females (1.85% males versus 1.62% females, P = 0.000). Based on odds ratio (OR) female have the chances of getting malaria 0.85% or 0.85 times than male (as categorical reference)
How do I interpret an odds ratio less than 1 in a logistic regression?
May I will be written male with a chance of 1 / 0.85 times or 1.2 times to get malaria compared than female as well?
or
The odds of malaria in male decreased by (1 - 0.85 ) 15% compared those in a female. Whatever on the dependent variable decreases. For each unit increase, it decreases by a multiple of (1 - OR )
Thank you in advance for your reply
Sincerely yours,
Hamzah
