Analysing election results
Locked
 
|
Dear All,
I'm currently analysing some election results. The data format is as follow: district districtclass age party sex percent A H 32 X M 35 A H 41 Y F 65 B L 21 Y M 70 B L 21 X M 10 B L 50 Z M 20 C L 46 X F 35 C L 35 Y F 45 C L 27 Z M 20 D H 49 Z M 40 D H 41 X M 60 E H 37 X M 30 E H 70 Y M 45 E H 61 Z F 25 F L 28 Y F 40 F L 30 Z F 60 These mean the District A is a Higher class district. It has 2 candidates. The first one is Male, 32, representing party X. He got 35% of total vote. The second one is Female, 41, representing party Y, and got 65% of total vote. District B is a Lower class district. It has 3 candidates, etc. Regardless the number of candidates, the sum of "percent" for any "district" must be 100. I would like to predict the "percent" by age, party and sex. Since "percent" is restricted for each "district", I'm not sure what model should I use for such data structure. I have tried the followings: UNIANOVA percent BY district party sex WITH age /DESIGN = district party sex age . UNIANOVA percent BY district party sex WITH age /RANDOM = district /DESIGN = district party sex age . UNIANOVA percent BY party sex WITH age /DESIGN = party sex age . Would anyone please to suggest which model should I use (and possbily other model not listed)? In addition, what should I do if I want to access the interaction effect between "districtclass" and "party"? Thanks a lot! Regards, Johnson |
Re: Analysing election results
|
Reformatting, only, of orginal post by Johnson Lau
<[hidden email]>. At 11:47 PM 3/27/2007, Johnson Lau wrote: >I'm currently analysing some election results. The data format is as >follow: (The following reformatted data brought to you by WRR and the editor Notetab, wondering why so much data does come through unrolled and nearly unreadable. I hope this may help.) district districtclass age party sex percent A H 32 X M 35 A H 41 Y F 65 B L 21 Y M 70 B L 21 X M 10 B L 50 Z M 20 C L 46 X F 35 C L 35 Y F 45 C L 27 Z M 20 D H 49 Z M 40 D H 41 X M 60 E H 37 X M 30 E H 70 Y M 45 E H 61 Z F 25 F L 28 Y F 40 F L 30 Z F 60 >These mean the District A is a Higher class district. It has 2 >candidates. The first one is Male, 32, representing party X. He got >35% of total vote. The second one is Female, 41, representing party Y, >and got 65% of total vote. District B is a Lower class district. It >has 3 candidates, etc. Regardless the number of candidates, the sum of >"percent" for any "district" must be 100. > >I would like to predict the "percent" by age, party and sex. Since >"percent" is restricted for each "district", I'm not sure what model >should I use for such data structure. I have tried the followings: > >UNIANOVA > percent BY district party sex WITH age > /DESIGN = district party sex age . > >UNIANOVA > percent BY district party sex WITH age > /RANDOM = district > /DESIGN = district party sex age . > >UNIANOVA > percent BY party sex WITH age > /DESIGN = party sex age . > >Would anyone please to suggest which model should I use (and possbily >other model not listed)? In addition, what should I do if I want to >access the interaction effect between "districtclass" and "party"? >Thanks a lot! |
|
Now I see what your data looks like.
Averaging percents is problematical unless the districts are the same size. I there any way you can get the actual counts? Art Kendall Social Research Consultants Richard Ristow wrote: > Reformatting, only, of orginal post by Johnson Lau > <[hidden email]>. > > At 11:47 PM 3/27/2007, Johnson Lau wrote: > >> I'm currently analysing some election results. The data format is as >> follow: > > (The following reformatted data brought to you by WRR and the editor > Notetab, wondering why so much data does come through unrolled and > nearly unreadable. I hope this may help.) > > district districtclass age party sex percent > A H 32 X M 35 > A H 41 Y F 65 > B L 21 Y M 70 > B L 21 X M 10 > B L 50 Z M 20 > C L 46 X F 35 > C L 35 Y F 45 > C L 27 Z M 20 > D H 49 Z M 40 > D H 41 X M 60 > E H 37 X M 30 > E H 70 Y M 45 > E H 61 Z F 25 > F L 28 Y F 40 > F L 30 Z F 60 > >> These mean the District A is a Higher class district. It has 2 >> candidates. The first one is Male, 32, representing party X. He got >> 35% of total vote. The second one is Female, 41, representing party Y, >> and got 65% of total vote. District B is a Lower class district. It >> has 3 candidates, etc. Regardless the number of candidates, the sum of >> "percent" for any "district" must be 100. >> >> I would like to predict the "percent" by age, party and sex. Since >> "percent" is restricted for each "district", I'm not sure what model >> should I use for such data structure. I have tried the followings: >> >> UNIANOVA >> percent BY district party sex WITH age >> /DESIGN = district party sex age . >> >> UNIANOVA >> percent BY district party sex WITH age >> /RANDOM = district >> /DESIGN = district party sex age . >> >> UNIANOVA >> percent BY party sex WITH age >> /DESIGN = party sex age . >> >> Would anyone please to suggest which model should I use (and possbily >> other model not listed)? In addition, what should I do if I want to >> access the interaction effect between "districtclass" and "party"? >> Thanks a lot! > >
Art Kendall
Social Research Consultants |
|
|
Basically they are the same size, and I have the actual counts too.
----- Original Message ----- From: "Art Kendall" <[hidden email]> To: <[hidden email]> Sent: Wednesday, March 28, 2007 8:06 PM Subject: Re: Analysing election results > Now I see what your data looks like. > > Averaging percents is problematical unless the districts are the same > size. I there any way you can get the actual counts? > > Art Kendall > Social Research Consultants > > > Richard Ristow wrote: >> Reformatting, only, of orginal post by Johnson Lau >> <[hidden email]>. >> >> At 11:47 PM 3/27/2007, Johnson Lau wrote: >> >>> I'm currently analysing some election results. The data format is as >>> follow: >> >> (The following reformatted data brought to you by WRR and the editor >> Notetab, wondering why so much data does come through unrolled and >> nearly unreadable. I hope this may help.) >> >> district districtclass age party sex percent >> A H 32 X M 35 >> A H 41 Y F 65 >> B L 21 Y M 70 >> B L 21 X M 10 >> B L 50 Z M 20 >> C L 46 X F 35 >> C L 35 Y F 45 >> C L 27 Z M 20 >> D H 49 Z M 40 >> D H 41 X M 60 >> E H 37 X M 30 >> E H 70 Y M 45 >> E H 61 Z F 25 >> F L 28 Y F 40 >> F L 30 Z F 60 >> >>> These mean the District A is a Higher class district. It has 2 >>> candidates. The first one is Male, 32, representing party X. He got >>> 35% of total vote. The second one is Female, 41, representing party Y, >>> and got 65% of total vote. District B is a Lower class district. It >>> has 3 candidates, etc. Regardless the number of candidates, the sum of >>> "percent" for any "district" must be 100. >>> >>> I would like to predict the "percent" by age, party and sex. Since >>> "percent" is restricted for each "district", I'm not sure what model >>> should I use for such data structure. I have tried the followings: >>> >>> UNIANOVA >>> percent BY district party sex WITH age >>> /DESIGN = district party sex age . >>> >>> UNIANOVA >>> percent BY district party sex WITH age >>> /RANDOM = district >>> /DESIGN = district party sex age . >>> >>> UNIANOVA >>> percent BY party sex WITH age >>> /DESIGN = party sex age . >>> >>> Would anyone please to suggest which model should I use (and possbily >>> other model not listed)? In addition, what should I do if I want to >>> access the interaction effect between "districtclass" and "party"? >>> Thanks a lot! >> >> > > |
|
|
In reply to this post by Art Kendall
They are basically the same size, and I have the actual counts too.
Thanks a lot. 2007/3/28, Art Kendall <[hidden email]>: > Now I see what your data looks like. > > Averaging percents is problematical unless the districts are the same > size. I there any way you can get the actual counts? > > Art Kendall > Social Research Consultants > > > Richard Ristow wrote: > > Reformatting, only, of orginal post by Johnson Lau > > <[hidden email]>. > > > > At 11:47 PM 3/27/2007, Johnson Lau wrote: > > > >> I'm currently analysing some election results. The data format is as > >> follow: > > > > (The following reformatted data brought to you by WRR and the editor > > Notetab, wondering why so much data does come through unrolled and > > nearly unreadable. I hope this may help.) > > > > district districtclass age party sex percent > > A H 32 X M 35 > > A H 41 Y F 65 > > B L 21 Y M 70 > > B L 21 X M 10 > > B L 50 Z M 20 > > C L 46 X F 35 > > C L 35 Y F 45 > > C L 27 Z M 20 > > D H 49 Z M 40 > > D H 41 X M 60 > > E H 37 X M 30 > > E H 70 Y M 45 > > E H 61 Z F 25 > > F L 28 Y F 40 > > F L 30 Z F 60 > > > >> These mean the District A is a Higher class district. It has 2 > >> candidates. The first one is Male, 32, representing party X. He got > >> 35% of total vote. The second one is Female, 41, representing party Y, > >> and got 65% of total vote. District B is a Lower class district. It > >> has 3 candidates, etc. Regardless the number of candidates, the sum of > >> "percent" for any "district" must be 100. > >> > >> I would like to predict the "percent" by age, party and sex. Since > >> "percent" is restricted for each "district", I'm not sure what model > >> should I use for such data structure. I have tried the followings: > >> > >> UNIANOVA > >> percent BY district party sex WITH age > >> /DESIGN = district party sex age . > >> > >> UNIANOVA > >> percent BY district party sex WITH age > >> /RANDOM = district > >> /DESIGN = district party sex age . > >> > >> UNIANOVA > >> percent BY party sex WITH age > >> /DESIGN = party sex age . > >> > >> Would anyone please to suggest which model should I use (and possbily > >> other model not listed)? In addition, what should I do if I want to > >> access the interaction effect between "districtclass" and "party"? > >> Thanks a lot! > > > > > -- Johnson Lau Research Assistant School of Public Health The Chinese University of Hong Kong Tel: (852) 2252 8705 Fax: (852) 2145 8517 |
«
Return to SPSSX Discussion
|
1 view|%1 views
| Free forum by Nabble | Edit this page |