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Hi,
I'm sure this is an easy question, but I would gladly appreciate the help.
I want to compare the proportion of women through two different phases. Let's say I have the following dataset:
data test;
input wom second_phase wom_2 wtps;
datalines;
1 1 1 3
1 0 . 2
1 1 0 2
0 1 0 2
0 0 . 5
1 1 1 3
1 1 0 4
1 1 . 4
1 0 . 5
0 1 0 1
0 0 . 3
1 1 . 2
1 1 1 2
1 1 0 2
;
run;
Using the weights, I can calculate that the weighted proportion of women in before second phase is equal to 72.5%, whereas the weighted proportion of women among those who qualified for the second phase (second_phase = 1) is equal to 42.1%.
What procedure could I use to be able to conclude that the differences between the weighted proportions of women across the two steps are statistically significant?
Thanks you in advance!
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Example using PROC FREQ
Paige Miller
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Thank you for your quick response.
The problem I have is that the missing values found in the variable wom_2 are excluding the observations in proc freq for both wom and wom_2.
Here is the code I use for the proc freq
proc freq data = test; tables wom * wom_2 / chisq; weight wtps; run;
And here is the output
Table of wom by wom_2 |
|
|
|
|
|
| wom_2 |
| Total |
|
| 0 | 1 |
|
wom |
| 3 | 0 | 3 |
0 | Frequency |
|
|
|
| Percent | 15.79 | 0 | 15.79 |
| Row Pct | 100 | 0 |
|
| Col Pct | 27.27 | 0 |
|
1 | Frequency | 8 | 8 | 16 |
| Percent | 42.11 | 42.11 | 84.21 |
| Row Pct | 50 | 50 |
|
| Col Pct | 72.73 | 100 |
|
|
| 11 | 8 | 19 |
Total | Frequency |
|
|
|
| Percent | 57.89 | 42.11 | 100 |
Frequency Missing = 21 |
|
|
|
|
Thank you
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Yes, data with missing values in one of the two category variables cannot be used in this analysis.
Paige Miller
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@Shawn08 wrote:
Is there any other alternatives I could use?
What do you mean? If you don't have the value for that observation how are going to account for that?
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Thanks
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