Posts by David Hood
Last ←Newer Page 1 2 3 4 5 Older→ First
-
For completeness, here are the Greens and NZF as well (both with median of 5, making Labour the most liked of the big 4 among non-voters)
-
Polity: Poll Soup, in reply to Danyl Mclauchlan,
Danyl, I'm seeing them as liking Labour more- Mainly because Labour has many fewer non-voters despising it than National has. Excluding the Don't Knows and Didn't Answers, the median Labour liking is 6 while the median National liking is 5.
-
Polity: Poll Soup, in reply to linger,
Wouldn’t “No-Vote” also include those under 18 at the time of the earlier
My first thought as well, but it would tell you where people entering the political process were placing themselves.
-
Here is 05-08,
What this isn't showing, because I am just focusing on the L,H<G<NZF vote and non-vote is the Labour/Maori Party/No Vote movements.
Yes, there was a one off (not in 11) big move from Labour to National. There was also a big move of Greens to Labour of about the same order as a percentage, but the Green donor size was smaller.
Still a lot larger proportion going to no vote from Labour than National, which adds up over time.
-
And here are the patterns from 2008 to 2011 from the NZES
The movement between National and Labour is pretty much a wash, but Labour lost a lot more of their support to the Did Not Vote as a percentage than National lost to that group (it was about the same in raw numbers, but national had more total support).
-
@Mr Mark. IF you are used to R, you can use the haven library to work with the SPSS format NZES. Last time it was discussed on Public Address, this was what I was using to convert the SPSS file to a csv via R.
if(!require(haven)){
install.packages(“haven”)
require(haven)
}fileLocation <- file.choose()
ds <- read_spss(fileLocation)
##this function is needed as there are missing value labels so can’t as_factor
applyValLabels <- function(column){
knownvalues <- attributes(column)$labels
valuelabels <- names(knownvalues)
lookup <- data.frame(column = as.character(knownvalues), valuelabels, stringsAsFactors = FALSE)
target <- data.frame(column = as.character(column), index=1:length(column), stringsAsFactors = FALSE)
combined <- merge(target, lookup, all.x=TRUE)
combined <- combined[order(combined$index),]
output <- as.character(column)
output[!(is.na(combined$valuelabels))] <- combined$valuelabels[!(is.na(combined$valuelabels))]
return(output)
}
newfile <- paste(fileLocation,”.csv”, sep="”)
for (eachName in names(ds)){
dseachName <- applyValLabels(dseachName)
}write.csv(ds, file=newfile, row.names = FALSE)
-
I agree with Ben here, when you look at the number not voting, and the change in that number between elections, that is a huge factor in tipping the deciders.
In the US at the moment, large turnout is seen as favouring Sanders and Trump (bringing in people who otherwise would not have contributed) and increased turnout more generally favouring democrats.
-
Most weekends this time of year I will spend a few hours wandering around in Orokonui Ecosanctury- I can't even really call it a day walk, but it is a chance to get to where there is no traffic noise, not encountering many other people, and a lot of birdsong. Photo from today
-
Access: Zika and microcephaly: things to…, in reply to Rosemary McDonald,
Orac is a surgeon and scientist, rather than agribusiness fan, so he has a pretty well developed professional judgement in evidence in health arguments.
-
Here is orac from sciblogs on the lavacide theory
