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Number of respondents mismatch in dataset and published report [message #9665] |
Mon, 02 May 2016 02:35  |
gkibria1@jhu.edu
Messages: 1 Registered: May 2016 Location: Baltimore, MD, USA
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Member |
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Hi
I was using the dataset of DHS 2014 of Bangladesh for a paper in my university. I already took permission of that. I was looking also at the published Bangladesh Demographic and Health Survey 2014 by the USAID. Wne I looked into the table 7.2 (page number 75) of the published report/book, I found that the number of respondents living in urban area was 4,709 and 12,149 in rural area. But when I was analyzing the dataset (in stata) BDIR70FL.DTA, I found the number was mismatch (6,1666 in urban and 11,693 in rural). Can you please help me to explain this?
Regards,
Kibria
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Re: Number of respondents mismatch in dataset and published report [message #12425 is a reply to message #12423] |
Mon, 15 May 2017 11:22  |
Bridgette-DHS
Messages: 3230 Registered: February 2013
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Senior Member |
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Following is a response from Senior DHS Stata Specialist, Tom Pullum and DHS Senior Research Associate, Shireen Assaf:
The difference between the 6167 and the 5047 is completely due to the use of weights. Below I will paste the results in Stata, first without weights and second with weights, using BDIR70FL.dta. I do this with iweight but you can also do it with pweight, svyset, and svy.
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To produce the weighted table in R, you would need to use the survey package. The following lines should help you get started.
library(survey)
# install and load the survey package
options(survey.lonely.psu="adjust")
# to fix the issue with strata with single PSU
# adjust will center the stratum at the population mean
data$wt = data$v005/1000000
# create the weight variable
mydesign<-svydesign(id=data$v021, data=data, strata=data$v023, weight=data$wt, nest=T)
# set the survey design using the uploaded data (named data), the cluster (v021), the strata (v023) , and the weight (wt).
# then use svymean or svytable to get the weighted proportions and frequencies of your variables. See the survey package documentation for further details.
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[Updated on: Mon, 15 May 2017 11:23] Report message to a moderator
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