It is that time of the year again! The Australian Conference on Personality and Individual Differences is being held in Sydney, and as usual I am sitting in front of my computer late the night before the conference, working on slides. I can only blame my low conscientiousness and organisation traits – despite my best intentions I never seem to get things done until the last minute. If you are interested in a copy of my presentation slides, you can download them here.
The paper which I am presenting builds on an earlier conference presentation I did in 2014, and subsequently published in 2015. This time I am looking at predicting the way people respond (not content, but their persistant pattern of responses) to personality measures, and the extent to which that pattern can be predicting using big data. After meeting Michal Kosinski at ACPID 2016, I was inspired to try my hand at some analysis using digital behaviours, more specifically Facebook likes. This started me on a journey to learn to use R, which is worthy of a blog post of its own.
If you want the brief findings, basically the particular pattern of responding I was interested in is acquiescence – the tendency to agree or disagree regardless of what the questions are about. It can be predicted well from digital behaviours, but if you drill down into the actual behaviours it appears that the machine learning algorithm is picking up on subtle differences in age as a predictor of acquiescence – exactly what I found in 2015.