A 2019 study by the University of Southern California’s Center for Body Computing followed 121 Marines as they went through an elite training course. This study was able to combine data collected beforehand with continuous collection during a 25-day course. The objective was to determine who would succeed. As failures have a high cost for both the organization and the individual, discovering reliable predictors can have a large positive impact on efficiency and readiness for the Marine Corps. Surprisingly, they found that physiological factors such as hours of sleep, step counts, and heart rates were not significant. Personality factors such as extroversion and positive affect were the strongest indicators. They also found times during the course when voluntary dropouts were more likely to occur (i.e. right before a physically challenging event). Only 56 of the participants passed.
The ability to collect data on large populations in a tightly controlled environment where there is no expectation of privacy makes the military a prime place to study people analytics. High dropout rates which result from pushing people to mental and physical extremes make a ripe environment for data collection and analysis. Another example of people analytics using the military is Angela Duckworth’s West Point study. Through her study of 11,000 West Point Cadets, she was able to feature engineer “grit” as a predictor of success. Unfortunately, selection bias is hard to overcome in this area due to the volunteer nature of the US armed forces and the lack of ethnic and gender diversity. However, I think this Marine Corps study only scratches the surface of what is possible with military experiments, and that learnings can be extrapolated to other organizations.
My main critique of USC’s study is their reliance on personality tests versus focusing on quantifiable data. They used surveys before training started to score candidates on six personality traits – openness, conscientiousness, extroversion, agreeableness, neuroticism, and ego resilience. During the training, they measured physiological data such as caloric intake, sleep duration, step count, and heart rate. It would be interesting to rely on quantifiable data to determine personality type or to expand your continuous data collections (voice data would be a great supplement). This study used a small sample size of a short course. I think that limiting the surveys could increase volunteer sizes and that connections between physiological and personality factors could be found. For example, a trainee who has a higher step count than his peers might be volunteering more and thus may have a higher degree of empathy. If voice data was collected, someone who speaks more during periods of duress would be showing leadership promise. Eliminating subjectivity is a major obstacle to implementation. It will take very concrete and defensible analytics to help make decisions about who gets to enter training and who gets failed. Personality tests and surveys will never make the cut.
Training has long been used as a selection process in the military. You get the people you want in the organization because a training program can filter out undesirable characteristics. But if this function can get fulfilled by analytics, how will training adapt? Growth, learning, and bonding are also fundamental goals of training that result from similar programs. Is a high degree of “fear of failure” necessary to get the most out of training? Out of an employee? During my training in the Marines, I felt like I had to prove myself every day. If selections are efficient and attrition rates dwindle, the selectivity and intensity of the training will be questioned. A corporate metaphor would be an organization where everyone gets promoted. It is hard to avoid the complacency trap. The means and the end are undesirable for surveys and subjective personality tests and military studies should shift focus to continuous collections of objective data. This shift can not only reduce bias, it can increase adoption.
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