Data in Social Science

The science aspect of social science work is central to improving social systems. Data inform whether we are moving in the right direction or need to adjust our course. However, data and science must not be the center of our work; we must keep focus on children, families, and individuals, whose experiences are nuanced and filtered through systems that hold innate bias, which rests within the data. By maintaining awareness of these biases and their potential, we can keep people at the center of our work and focus on the true causes of injustice and pain that many families deal with. Lack of awareness can be a slippery slope to believing that individuals or families are themselves at fault for their situations, be it lack of housing, interpersonal violence, or other abuse. With data that are conscious of systemic issues and biases, we can examine the true issues at hand, such as racism, financial instability, and ableist policies. 

What good are any manner of properly applied scientific formulas if the data we feed into them are inaccurate? Can we truly know the effects that being removed from the home have on girls if we do not take into account their status as cisgender or transgender individuals, or whether they are actually nonbinary instead? Many of these problems come back to systems within communities themselves, whose collection methods have been in place for decades and are in sore need of updates. Often, external analysts have little control over these methods, and we must expose these data gaps as we aim for accuracy; doing so can push change in the necessary direction.

Admitting to our lack of knowledge is one of the first steps in exposing holes in the systems that we work within. Once we have acknowledged these limitations, we can begin to bridge the gap between the data we have and the data we need in ways that will encourage the unraveling of systemic biases.