There are so many aspects that underpin success in our career, it might seem impossible to identify the single most important element. That is however, exactly what a team of researchers from Temple University claim to have done in a recently published paper.
Armed with a machine learning system, they were able to rank the most important predictors of success, which included things such as education and our ability to delay instant gratification.
This ability to tap into our willpower was first brought to public attention via the famous marshmallow test, which tracked children over their lifetime after they were first tested to see whether they could resit gobbling a marshmallow now for the reward of more marshmallows later. This showed that those children who were able to resist the instant temptation seemed to do better in life.
The new study was able to identify the key factors after a thorough and robust analysis of a large dataset that was only really possible via machine learning.
The needle in the haystack
“All sorts of things predict income. We knew that this behavioral variable, delay discounting, was also predictive — but we were really curious how it would stack up against more common-sense predictors like education and age. Using machine learning, our study was the first to create a validated rank ordering of age, occupation, education, geographic location, gender, race, ethnicity, height, age and delay discounting in income prediction,” the authors explain.
The usual methods applied by psychologists have not allowed for comparing multiple factors at once. By using machine learning, the team were able to collect huge amounts of data on over 2,500 participants to produce a training and test data set.
Whilst some of the findings were intuitive, such as education and occupation being high predictors of future income, as were location and gender, there were some that were less intuitive, with the ability to delay gratification foremost among them. The team believe their work is indicative of a wider trend in social research.
“This was amazing because it allowed us to check our findings and replicate them, giving us much greater confidence that they were accurate. This is particularly important given the recent wave of findings across science that do not seem to replicate. Using this machine learning approach could lead to more research that replicates — and we hope this spurs the use of more sophisticated analytic approaches in general,” they explain.
It’s worth pointing out that the data was pulled only from American participants, and the team are curious about whether similar results would emerge from different cultures. This will probably be their next step. For now though, it is perhaps worth trying to install an ability to delay gratification if we want a successful career.
