The first step to taking a Targeted Universalist approach is to disaggregate your data. This is crucial to understand how each of your sub-populations is doing relative to the goal. For example, we might have a goal that 95% of our students read at grade level. Overall, we see that 80% of our students are reading at grade level, but when we disaggregate by gender, race, ethnicity, and immigrant status we might see that some groups are closer to the goal than others.
Our next step will be to focus on each group of students to understand what assets and constraints are influencing their success. For each of our sub-populations, these assets and constraints would be included in the opportunity section (or problem statement or needs section) of your logic model.
Now, we design our programs. For example, a program that is specifically designed for immigrant students might acknowledge that materials in English are a barrier to their learning and the solution might be linguistically diverse approaches. That programs will have its own implementation strategy, process measures that are connected to serving that specific population well, and short-term outcome measures that connect to our long term outcome -- % reading at grade level -- and target – 95%. Targeted Universalism is an important approach that transcends universal approaches that ignore differences in groups’ access and opportunity and therefore perpetuate inequality, and targeted programs that are subject to the criticism that they unfairly exclude some groups who might also need assistance or that they divert resources to marginalized groups who might already be burdened with stereotypes.