Home » Topics » General » gender matching interviewer / participant in Zimbabwe & Burundi (Zimbabwe & Burundi appear to have mismatched genders for interviewer/respondent?)
Re: gender matching interviewer / participant in Zimbabwe & Burundi [message #23555 is a reply to message #23545] |
Wed, 06 October 2021 07:35 |
Bridgette-DHS
Messages: 3203 Registered: February 2013
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Senior Member |
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Following is a response from DHS Research & Data Analysis Director, Tom Pullum:
Thank you for the positive feedback on the interviewer effects work. Much appreciated!
You are right that the Zimbabwe survey indicates that men were interviewing women and women were interviewing men, and this is not explained in the same way as in the Burundi survey. I ran the following Stata lines to get lists of which interviewer codes were involved (I know you use R but this is easier for me):
* Zimbabwe
cd e:\DHS\DHS_data\scratch
use "C:\Users\26216\ICF\Analysis - Shared Resources\Data\DHSdata\ZWFW71FL.DTA", clear
keep fw101 fw105
rename fw101 interviewer_id
rename fw105 interviewer_sex
sort interviewer_id
save tempfw.dta, replace
use "C:\Users\26216\ICF\Analysis - Shared Resources\Data\DHSdata\ZWIR72FL.DTA", clear
keep v001 v002 v003 v012 v024 v025 v028 v030
rename v028 interviewer_id
sort interviewer_id
merge interviewer_id using tempfw.dta
tab _merge interviewer_sex
keep if _merge==3
tab interviewer_sex
tab interviewer_id if interviewer_sex==1
use "C:\Users\26216\ICF\Analysis - Shared Resources\Data\DHSdata\ZWMR72FL.DTA", clear
keep mv001 mv002 mv003 mv012 mv024 mv025 mv028 mv030
rename mv028 interviewer_id
sort interviewer_id
merge interviewer_id using tempfw.dta
tab _merge interviewer_sex
keep if _merge==3
tab interviewer_id if interviewer_sex==2
I suspect that all of these irregular cases can be explained by a combination of coding errors in the FW data or misuse of interviewer ID codes. I asked Trevor Croft and he gave the following detailed interpretation. Nevertheless, this issue may complicate your analysis. You may want to simply drop these cases from your analysis. At least you can be sure that the inconsistencies were not edited out, giving a false impression that the data were perfect!
From Trevor Croft:
Quote:Here is what I could figure out:
Interviewer 803 only did men's interviews and is almost certainly a man. This looks like a coding error for the interviewer's sex.
Interviewer 920 is a supervisor code, and it appears that the person using the code at the beginning of the survey was female, but left the interviewing teams. The replacement supervisor was male and was originally interviewer 903 in a different team. 903 worked as a male interview in July and the beginning of August, but then was supervisor (using code 920) but was also conducted some men's interviews. This is not what should have happened. It can be corrected by recoding interviewer code 920 in the men's data as code 903.
Interviewers 201 and 207 are not clear. I think that there was probably some confusion in the team in terms of interviewer numbers and for a couple of clusters each a female interviewer used a male interviewer's number.
It is definitely possible that v028 and mv028 may be incorrect. Interviewers starting data collection in the cluster first provide the team number, then from within a list for the team they select their interviewer number. It is almost impossible for them to get the team number wrong as many things would fail, but it is definitely possible for them to confuse interviewer numbers, and we see a smattering of other cases that appear to be that type of problem. For 201 and 207 it appears that these were used incorrectly for two clusters each. It is possible that a couple of temporary interviewers were given the codes 201 and 207. I'm guessing for these two.
I might just mention that historically a few surveys have permitted men to interview women and women to interview men. For example the first Ghana DHS in 1988 permitted that as it was standard practice for their surveys at the time.
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