Hi!
I am working on a data set that includes 10 rounds. It does not have a specific round/time info, but it has interview_r1, interview_r2, ...interview_r10 for those households interviewed in those rounds(1.Yes, 0.No). I was wondering if any chance to generate a variable to show round to include in the analysis that I can cover changes over time. When I try something like gen round=1 if interview_r1==1, replace round=2 if interview_r2==1, it turns out weird as same households are cover in different rounds. It looks as below.
household_id interview_r1 interview_r2 interview_r3 interview_r4 interview_r5
010101088800910007
010101088800910017 1. YES 1. YES 1. YES 1. YES
010101088800910026
010101088800910029 1. YES 1. YES 1. YES
010101088800910038
010101088800910046
010101088800910054 1. YES 1. YES 1. YES
010101088800910062
010101088800910070
010101088800910082 1. YES 1. YES 1. YES 1. YES 1. YES
010101088800910093 1. YES 1. YES 1. YES
010102088801010034 1. YES 1. YES
010102088801010035 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010048 1. YES 1. YES 1. YES
010102088801010068
010102088801010090 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010104 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010113 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010136
010102088801010148 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010161
010102088801010173 1. YES 1. YES 1. YES 1. YES 1. YES
010103088800709008
010103088800709016
010103088800709024
010103088800709032
010103088800709040
010103088800709048
010103088800709056 1. YES 1. YES 1. YES 1. YES 1. YES
010103088800709064
010103088800709072 1. YES 1. YES 1. YES 1. YES 1. YES
010103088800709080
010104010100101012
010104010100101028 1. YES 1. YES 1. YES 1. YES 1. YES
010104010100101044 1. YES 1. YES 1. YES 1. YES 1. YES
010104010100101060 1. YES 1. YES 1. YES 1. YES 1. YES
010104010100101076 1. YES 1. YES 1. YES 1. YES 1. YES
Beside, some variables given for specific round (e.g. number of female_r1, number of female_r2..). In the analysis should I add them seperately as given, or an alternative and more practical way exists?
Thank you in advance!
I am working on a data set that includes 10 rounds. It does not have a specific round/time info, but it has interview_r1, interview_r2, ...interview_r10 for those households interviewed in those rounds(1.Yes, 0.No). I was wondering if any chance to generate a variable to show round to include in the analysis that I can cover changes over time. When I try something like gen round=1 if interview_r1==1, replace round=2 if interview_r2==1, it turns out weird as same households are cover in different rounds. It looks as below.
household_id interview_r1 interview_r2 interview_r3 interview_r4 interview_r5
010101088800910007
010101088800910017 1. YES 1. YES 1. YES 1. YES
010101088800910026
010101088800910029 1. YES 1. YES 1. YES
010101088800910038
010101088800910046
010101088800910054 1. YES 1. YES 1. YES
010101088800910062
010101088800910070
010101088800910082 1. YES 1. YES 1. YES 1. YES 1. YES
010101088800910093 1. YES 1. YES 1. YES
010102088801010034 1. YES 1. YES
010102088801010035 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010048 1. YES 1. YES 1. YES
010102088801010068
010102088801010090 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010104 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010113 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010136
010102088801010148 1. YES 1. YES 1. YES 1. YES 1. YES
010102088801010161
010102088801010173 1. YES 1. YES 1. YES 1. YES 1. YES
010103088800709008
010103088800709016
010103088800709024
010103088800709032
010103088800709040
010103088800709048
010103088800709056 1. YES 1. YES 1. YES 1. YES 1. YES
010103088800709064
010103088800709072 1. YES 1. YES 1. YES 1. YES 1. YES
010103088800709080
010104010100101012
010104010100101028 1. YES 1. YES 1. YES 1. YES 1. YES
010104010100101044 1. YES 1. YES 1. YES 1. YES 1. YES
010104010100101060 1. YES 1. YES 1. YES 1. YES 1. YES
010104010100101076 1. YES 1. YES 1. YES 1. YES 1. YES
Beside, some variables given for specific round (e.g. number of female_r1, number of female_r2..). In the analysis should I add them seperately as given, or an alternative and more practical way exists?
Thank you in advance!

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