Use Data to Create Consensus



Use Data to Create Consensus

Chris Scharenbroch, Senior Research Associate

Lots of different things can happen to a young person after he or she is arrested or referred to law enforcement. He may go home or he may be held in detention until court; his charges may be dropped or they may be formally processed; a judge may dismiss charges, order supervision, or sentence him to stay in a secure facility.

How youth move through a system depends largely on local policy. These policies are often developed through a consensus-building process that relies on local expertise from agency administrators, attorneys, law enforcement, elected officials, and judges. This stakeholder group works with the intention to develop or revise policies to best reflect community values about safety.

This process, however, results in local policies that can be quite different from place to place. For example, in the United States just over one in every three youth placed out of home (whether in detention awaiting a court hearing or in a secure facility after disposition) are held for “person offenses” such as assault, robbery, and other offenses in which people were threatened or hurt. However, this rate varies widely across the country, from as low as 12% in Wyoming and up to 54% in Massachusetts.1 Having a lower or higher rate does not necessarily mean one can draw a particular conclusion about that state system; it simply tells us, of all youth placed out of the home how many committed a person offense. Without more information, we don’t know exactly why they were placed, or if something needs to change. However, to start learning the answers to those questions it’s helpful for everyone to have objective data to look at.

At NCCD, we often ask our jurisdictional partners who develop these policies how their community compares. Of the youth in placement in their own community, how many might they guess are held for a person offense? Is the rate similar to the national average of 1 in 3, or lower or higher? It is also interesting to ask stakeholders how they arrived at their guess. Is it based on a hunch, or is it based on data? If it’s not based on data, we think it should be!

Imagine a room full of policy makers convening to talk about youth in placement, having only their professional experience to guide the conversation. How might an administrator’s perception of incarcerated youth differ from that of a sheriff or a prosecutor? Some stakeholders may believe that they incarcerate only the most violent youth, while others might have a different perspective. The truth is, different people bring different perspectives, and the only way to objectively understand the population is to use data analytics. 

Data analytics can provide a factual description of all youth within the system. These data are often easily accessible in local administrative systems. For agencies accustomed to data-informed practice, it may take only a few minutes for a data analyst or technical assistance provider to describe exactly who is incarcerated and provide any number of characteristics that describe the population.

Now imagine the same stakeholder group meeting, only this time everyone has in front of them a descriptive profile of incarcerated youth. This profile describes all youth in placement by offense. Regardless of whether the actual rate is in line with the national average or quite different, this information grounds the conversation. The “who” we incarcerate question is no longer up for debate—the facts are on display. The group is now free to focus on the next question: for example, for which youth does our community feel out-of-home placement is an appropriate option?

During the consensus-building process, it can be difficult to separate our individual narratives from what is happening at the system level. Data analytics are necessary to establish this kind of common understanding. Using data to ground the conversation guards against human beings’ natural tendency to let the most powerful narratives shape our perceptions of the system, and allows us to focus on how the system is functioning as a whole.  

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