Posts that use voting behaviour data

I try out biterm topic modelling on a free text question in the 2017 New Zealand Election Study about the most important issue in the election.

My forecasts for the 2020 New Zealand general election are out, and predict a comfortable win for Jacinda Ardern's Labour Party either alone or in coalition.

I release an improved and updated version of my crosstab webtool for exploring the New Zealand Election Study data, now covering 2017 as well as 2014, and letting the user explore relationship between party vote and a range of attitudes, experiences and demographics.

The polls did worse than usual in predicting the Australian federal election, but the pundits were far worse than the polls.

A few small explorations of idiosyncracies with polls, a couple of days before the Australian federal election.

I familiarise myself with the 2016 Australian Election Study, a wonderful source of individual level data on attitudes and behaviour relating to voting.

I've put up a page with my current Australian federal election forecasts for the House of Representatives, which I'll keep up to date until the election comes.

I explore the data on two-party-preferred voting swings in Australian federal elections and tentatively introduce the ozfedelect R package.

I tidy up Australian polling data back to 2007 and produce a statistical model of two-party-preferred vote for the coming election.

I update the nzelect R package with the latest New Zealand polling data, and use a generalized additive model to look for a seasonal impact on support for the current government.

New Zealand's election results have been released and were within the range of my probabilistic predictions. The pollsters did a good job.

A new version of the nzelect R package is on CRAN, with election results by voting location back to 2002, and polls up to the latest election. I show how to extract and understand the "special" votes and how they are different to advance voting.

I adjust my state-space model of New Zealand voting behaviour to allow for the house effect of one of the pollsters to change from the time they started including an on-line sample, and get some interesting results.

New Zealand electoral polls going back 15 years

I introduce a new web app that allows nons-specialists to explore voting behaviour in the New Zealand Election Study, and reflect on what I've done so far with that data.

I explore the demographic characteristics of who voted (and who didn't), out of people on the electoral roll, in the 2014 New Zealand general election. I use multiple imputation and a generalized linear model with a quasibinomial response. The people who vote tend to have characteristics associated with doing ok out of society (owning a home, having a partner, university qualifications, etc).

I revisit the state space model of Labor party vote leading up to the 2007 Australian election; and a re-think about total survey error in the context of polling data leads to a more stable, less wiggly underlying state of voting intention. Also, vectorization in Stan leads to much faster estimation.

As part of familiarising myself with the Stan probabilistic programming language, I replicate Simon Jackman's state space modelling with house effects of the 2007 Australian federal election.

Sankey charts based on individual level survey data are a good way of showing change from election to election. I demonstrate this, via some complications with survey-reweighting and missing data, with the New Zealand Election Study for the 2014 and 2011 elections.

Introducing a Shiny web tool for exploring individual characteristics and party vote in the 2014 New Zealand general election.

I work through a fairly complete modelling case study utilising methods for complex surveys, multiple imputation, multilevel models, non-linear relationships and the bootstrap. People who voted for New Zealand First in the 2014 election were more likely to be older, born in New Zealand, identify as working class and male.

My New Zealand Election Forecasts web page is up; and I have some reflections on election day randomness, and on quality control.

I use generalized additive models to explore "house effects" (ie statistical bias) in polling firms' estimates of vote in previous New Zealand elections.

Version 0.3.0 of the nzelect R package now on CRAN includes historical polling data and a few convenience functions

I have a quick look at the polling data used by the FiveThirtyEight website in predicting the USA presidential election results

Individual level data on voting behaviour are freely available from the New Zealand Election Study and everyone should have a go at analysing them!

The nzelect R package is now available on CRAN; so far it has aggregate results by voting place for the New Zealand 2014 general election.

Locations with more self employed, people with Bachelor degrees, no religion, and people living overseas five years ago were more likely to vote Green over Labour in the 2014 New Zealand General Election, and locations with more ethnically Asian and Pacific people, people born in New Zealand, and people with no qualification returned higher votes for Labour over the Greens.

I talk through some exploratory analysis of voting location with a Shiny application using the {nzelect} New Zealand General Election 2014 data.

I explain the structure and techniques behind building the nzelect R package, which has New Zealand election results, in case anyone is interested or wants to adapt the process for other packages that rely on preparatory data munging.

I introduce a new R package, with tidy versions of New Zealand election data (so far only for the 2014 general election). We confirm and discuss some well-known results such as the tendency for the Labour Party to apparently under-perform on party vote; and identify patterns at the Territorial Authority and Regional Council level that are not usually available due to how voting behaviour is normally classified and reported on.