Disaggregating data by subgroups is essential in evaluating a program's effectiveness because it helps to

• A. It reduces data collection
• B. It standardizes testing
• C. It helps identify disparities and tailor supports to specific populations
• D. It hides differences

Answer: (C)

Breaking results down by subgroups reveals how different groups are affected by a program. When you look only at the overall average, you can miss important differences in outcomes among groups defined by characteristics like race, gender, income, or location. Disaggregating data shows where disparities exist, who benefits less, and where the program may need adjustments. With that information, you can tailor supports, resources, or strategies to the specific needs of those populations, helping the program be more effective for everyone.

Disaggregation isn’t about reducing data collection or standardizing testing; it’s about analyzing results by subgroup. It also doesn’t hide differences—doing the breakdown makes differences visible, which is why this approach is so valuable for improving equity and effectiveness.