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Home » Countries » Nepal » Nepal dataset 2011 (Very small numbers for some variables)
Nepal dataset 2011 [message #21738] Mon, 14 December 2020 18:00 Go to next message
vpatil is currently offline  vpatil
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Registered: March 2019
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I am using the questions on whether men and women justify violence across different surveys. I found a consistency in numbers across 2001, 2006, and 2016 datasets but the numbers in 2011 are, abnormally, low. Was there a change in the way questions were asked or the way the answers have been compiled?

Also, the number of men who were asked the questions on justification is very small (110) for the men dataset and 54 in the couple 2011 dataset. Is there any reason why these numbers are so small?
Re: Nepal dataset 2011 [message #21761 is a reply to message #21738] Tue, 15 December 2020 09:29 Go to previous messageGo to next message
Bridgette-DHS is currently offline  Bridgette-DHS
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Following is a response from DHS Research & Data Analysis Director, Tom Pullum:

You will have to look carefully at the wording and skips in the questionnaires from these four surveys. The main reports include the English versions of the questionnaires in an appendix.

Re: Nepal dataset 2011 [message #21807 is a reply to message #21761] Wed, 23 December 2020 03:59 Go to previous messageGo to next message
vpatil is currently offline  vpatil
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Registered: March 2019
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Okay, I understand that there was a skip pattern for the questions asked. Is there a way to estimate what would be the percentage of men who would have said yes if all were asked this question- i.e. instead of having the denominator as 110 have the entire sample population as the denominator (about 4121)? Nepal 2011 men dataset
Re: Nepal dataset 2011 [message #21815 is a reply to message #21807] Wed, 23 December 2020 06:54 Go to previous message
Bridgette-DHS is currently offline  Bridgette-DHS
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Following is a response from DHS Research & Data Analysis Director, Tom Pullum:

I can only give a generic answer, because I don't have time to go over the specific skip pattern. You can "back out" of the skips and filters to get at a larger denominator. For an analogy, if a question "have you had any births" is followed by "how many" for those who answer "yes", then the denominator would be the number who were asked the first question. If they answered it with "no" then you assign 0 to "how many". Usually, that kind of skip is absorbed during data processing, to produce a single variable, for example the number of children ever born, including 0. But sometimes it is not done during data processing and you have to do a recode yourself.

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