Applying statistics and data science 'in the wild'
I write about applications of data and analytical techniques like statistical modelling and simulation to real-world situations. I show how to access and use data, and provide examples of analytical products and the code that produced them.
I play around with population-weighted income inequality of countries with data from the World Development Indicators, re-creating (with some amendments) some graphics from Branko Milanovic's recent book "Global Inequality".
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.
I look at the interaction between deprivation, being Māori, and family violence - combining data from the New Zealand census, the New Zealand index of deprivation, and the Family Violence Death Review Committee.
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.
Excited that New Zealand's Government Statistician is promoting reproducibility and open access to code, tabular outputs and research products from research with confidentialised microdata.
For choropleth maps showing the whole world, we don't need to stick to static maps with Mercator projections. I like rotating globes, and interactive slippery maps with tooltips.
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.