Measurement, evaluation, and accountability

Why it matters

Legacy data systems were built for billing and eligibility, not equity analysis. Good measurement protects private learning, refuses invented targets, and reports what the evidence does and does not show.

Questions to begin with

• What would change if we knew the answer?
• What is the baseline, the denominator, and the smallest group we will report?
• What do people's experiences add that the numbers cannot?
• How do we measure the program without scoring or ranking people?

Work you can take forward

Plan an evaluation or set of measures

A logic model with baseline, mixed-method evidence, disaggregation rules, small-group protection, and limits stated plainly.

Data, research, and quality · Program and policy staff · Division and administration leadership

• Measuring whether equity work is working, without scoring people — program page /library/pn-measurement-without-surveillance

Ask: We want to know whether a service change reduced disparities. How do we set that up honestly?

Read and present disaggregated data responsibly

Clear statements of what the data shows, what it does not, and which small groups are protected from identification.

Data, research, and quality · Communications and language access

• Measuring whether equity work is working, without scoring people — program page /library/pn-measurement-without-surveillance
• Six commitments for working in partnership — program page /library/pn-partnership-spine

Ask: How do I present service data by race and language without exposing small groups or overclaiming causes?

Tools and frameworks

• Tool card: Minnesota Equity Analysis Toolkit — The State of Minnesota's official equity analysis toolkit, published by Minnesota Management and Budget, used with the DHS Equity Policy. Public. Permitted use: cite, link, follow. The policy owner determines when it is required.