Commentary: The Critical Role of Measurement (and Space Elevators) in the Study of Child Development

A few years back I became friends with a research scientist who was a member of a molecular physics lab on campus. We mostly spent our time arguing about baseball (the pitcher should always bat), but occasionally we would talk about work. He was researching the development of a space elevator that consisted of a satellite placed in geosynchronous orbit while tethered to the ground, allowing an elevator to deliver payloads to space. He would talk about things like nanotubes, microfabrication, noncovalent intermolecular forces, and molecular assemblers, and then would ask about my own current projects. After hearing about his work, I would sheepishly tell him about how I was trying to develop a measurement model for childhood depression. I described how we had gathered binary data indicating the presence or absence of symptoms obtained from a sample of children followed over time and that we were trying to map the binary items onto a continuously distributed score to model trajectories of depression. I felt so sheepish because our empirical data were nothing more than a rectangle of zeros and ones, the underlying mathematics did not go much past high school calculus, and the requisite computer programming constituted a few dozen lines of code at best. Yet at the end of my description my friend laughed, shook his head, and said that he was glad that he had the easier problem on which to work; he had no desire to tackle something as challenging as trying to measure depression in children.