| Course | PNP 6003 Leading and Managing Public and Nonprofit Organizations |
|---|---|
| Module | Module 3 |
| Paper type | Performance measures and decision paper |
| Length | 1,250 words, about 5 pages plus title and reference pages |
| Format | APA 7 student paper |
| School | American College of Education |
| Program | Doctor of Business Administration |
| Updated | October 2026 |
Free sample paper for PNP 6003 Module 3
Where Should $90,000 Go? Performance Measures and a Data-Informed Decision for a Spokane Free Clinic
Student Name
American College of Education
PNP6003: Leading and Managing Public and Nonprofit Organizations
Module 3 Assignment
Instructor Name
October 26, 2026
Introduction
ACE frames this course around turning good intentions into action through data-informed decisions. Module 2 gave the Spokane free clinic a plan with three goals: show and improve health results, stabilize clinical capacity and spread financial risk. This paper does two things. It defines the performance measures that will track the plan, and it uses the first data from those measures to make a real decision: how to spend a one-time grant of $90,000 from a local foundation that has left the use to the clinic's discretion within a year.
Choosing the Measures
Measures serve different purposes, and a measure chosen to impress funders may be useless for improving care (Behn, 2003). The clinic needs measures it can use to learn and improve as well as to report. Five were chosen, one or two for each goal, and each comes with a written definition, a named source of data and a set reporting interval. The set measures results the clinic can credibly influence rather than distant community outcomes (Ebrahim & Rangan, 2014), and it puts mission results ahead of financial ones while still tracking the money that makes them possible (Kaplan, 2001).
Hypertension control: proportion of enrolled hypertension patients with a latest reading under 140/90, from the new registry, monthly. Diabetes control: share of enrolled diabetes patients whose most recent A1C is below 8%, from the registry, quarterly. Chronic care no-show rate: share of scheduled chronic disease visits missed, from the scheduling system, monthly. Turned-away requests: number of patients who called or walked in and could not be seen that week, from a front-desk log, monthly. Funding concentration: the portion of annual income supplied by the clinic's top three funders, from the finance report, quarterly.
What the Pilot Showed
A two-month pilot of the registry and logs, run with one clinician team, produced the clinic's first look at its own results. Among 214 enrolled hypertension patients with a recent reading, 51% were below 140/90. Among 168 enrolled diabetes patients, 46% had an A1C below 8%. The no-show rate for chronic disease visits was 29%, far higher than staff had estimated. The front desk turned away an average of 23 requests a week, concentrated on Monday mornings and on the two evenings the clinic is closed. The pilot changed the conversation: the clinic's largest gap was not how good its care was when patients came, but how often patients with chronic disease did not come at all. These figures are early and come from one team, so they are treated as indicators rather than settled facts.
What the Data Cannot Yet Say
The pilot left several questions open. It did not record why patients missed visits, so the clinic cannot yet tell whether transportation, work schedules, cost of medications or simply forgetting is the main cause. It measured control at one point in time, so it cannot show trends. And it did not capture the experience of patients who were turned away, some of whom may have gone to an emergency department. Rather than delay the decision until these gaps close, the team chose to act on what the pilot did show and to design the chosen option so that it would collect the missing information: a community health worker calling patients would learn why they miss visits, and that knowledge would improve the clinic's next decisions as well as this one.
The Decision and the Options
Three uses of the $90,000 were proposed by staff and board members. Option A would fund two additional evening sessions a week for a year, with a paid nurse practitioner and support staff, to reduce turned-away requests. Option B would fund a part-time community health worker for a year to call patients before chronic disease visits, help with transportation and follow up after missed appointments, aimed at the no-show rate. Option C would expand the pharmacy's stock of low-cost generic medications, reducing reliance on donated supplies that have become less predictable. All three serve the mission; the question is which does the most for the plan's goals with one year of money.
Agreeing on What Matters
Before any option was scored, the leadership team settled on four criteria, so that the criteria would not be tailored to a favorite. Expected effect on health results was weighted 40%, because the plan's first goal is to show and improve outcomes. Sustainability after the grant year was weighted 25%, since a program that collapses when the grant ends creates new problems. Effect on clinic capacity was weighted 20%. Ease of measuring the result was weighted 15%, because funders and the board need to see whether the money worked. Three staff members and two board members each gave every option a score between one and five against each criterion, working independently, and the ratings were averaged.
Scoring
Option B, the community health worker, scored highest, with a weighted total of 3.96. It scored 4.6 on expected health effect, because reducing a 29% no-show rate among patients already enrolled reaches the people whose blood pressure and A1C the clinic is trying to improve; 3.0 on sustainability, because the role would need new funding after a year; 3.4 on capacity, since filled appointment slots use existing capacity better; and 4.6 on ease of measurement, because the no-show rate is already tracked monthly. Option A scored 3.47, strong on capacity but weaker on sustainability, since evening sessions with paid staff cost the most to continue. Option C scored 3.05, valuable for reliability but less directly tied to the measured gaps.
How Sturdy Is the Choice?
Since any set of weights reflects judgment, the team reran the scoring with two alternative weightings. When sustainability was raised to 40% and health effect lowered to 25%, Option B still led, at 3.72 against 3.38 for Option A. Only when capacity was weighted above 40% did Option A move ahead, a weighting no one on the team defended. The decision is therefore robust to reasonable disagreement about priorities. One caution remains: the pilot data come from a single team over two months, and the no-show rate could differ across teams. The plan addresses this by measuring the no-show rate clinic-wide in the first month of the grant, before the community health worker's caseload is set.
The Decision and How It Will Be Judged
The clinic will use the grant to fund a community health worker for twelve months. Success will be judged by three measures already defined: the chronic care no-show rate, with a target of 18% by month twelve; hypertension control, with a target of 58%; and diabetes control, with a target of 52%. Results will be reported quarterly to the foundation and the board. If the no-show rate falls but control does not improve, the clinic will learn that attendance is not the main barrier, which is itself useful. Sustaining the role after the grant will be the first objective of the funding diversification goal, giving the strategic plan and this decision a common thread.
Conclusion
Five measures now track the clinic's strategic plan, and their first data revealed a 29% no-show rate among chronic disease patients. Using four criteria agreed in advance, weighted and scored independently, the leadership team chose a community health worker over evening hours and pharmacy expansion, and the choice held under alternative weightings. Module 4 turns to a dilemma that this decision raises with one of the clinic's largest funders.
References
Behn, R. D. (2003). Why measure performance? Different purposes require different measures. Public Administration Review, 63(5), 586-606. https://doi.org/10.1111/1540-6210.00322
Ebrahim, A., & Rangan, V. K. (2014). What impact? A framework for measuring the scale and scope of social performance. California Management Review, 56(3), 118-141. https://doi.org/10.1525/cmr.2014.56.3.118
Kaplan, R. S. (2001). Strategic performance measurement and management in nonprofit organizations. Nonprofit Management and Leadership, 11(3), 353-370. https://doi.org/10.1002/nml.11308
Reading the PNP 6003 Module 3 instructions
The third module of PNP 6003 tends to bring data into leadership decisions. Prompts usually ask you to identify performance measures that fit your organization's goals and to use data, from your organization or credible external sources, to make or evaluate a decision. Expect to explain why each measure was chosen, where its data come from and how often it will be reviewed. For the decision, state the options, the criteria you used and how the evidence pointed to a choice. Acknowledge the limits of your data honestly. Graders want to see the reasoning, so make the steps between evidence and decision explicit. Where possible, state targets and the date by which you expect to reach them. Keep the number of measures small.
How the PNP 6003 Module 3 example is put together
Measures come first, chosen for learning as well as reporting and defined with source and frequency. A two-month pilot then supplies the clinic's first outcome and attendance data, and the paper explains how a high no-show rate reframed the problem. Three uses of a grant are described, four criteria are agreed and weighted before scoring and independent scores are averaged. The scoring section reports each option's total and the reasons behind it. A sensitivity section reruns the scores under different weights, and the decision section sets targets and explains what the clinic would learn if attendance improved without better control. Funding the role beyond the grant is linked to the plan's third goal.
Reading the PNP 6003 Module 3 rubric
Data-informed decision papers are graded on the quality of measures and the transparency of reasoning. Faculty look for measures tied to goals, defined precisely and drawn from realistic sources, and for decisions in which options, criteria and evidence are visible. Criteria set before scoring, independent judgments and a test of how sensitive the result is to assumptions all earn credit. Papers that state data limitations and plan how to check the decision later show maturity. Purely intuitive decisions dressed in numbers lose points. Clear organization, citations of measurement sources and APA 7 formatting complete the assessment. Targets with dates make a decision easy to evaluate later. Independent scoring is a plus.
PNP 6003 Module 3 help: mistakes that cost points
Many students know what their organization should do but struggle to show the data and reasoning a grader expects. If your measures are vague or your decision reads like a preference, our writers can help you build a transparent analysis. Share your strategic goals, whatever data you have and the prompt; what you receive is a measurement set and a decision analysis with criteria, scoring and a sensitivity check. Public agencies, clinics and nonprofits all face choices like this. A clear decision trail is persuasive to graders and to boards alike. Scoring sheets are included.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official American College of Education document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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PNP 6003 Module 3 questions, answered
What does PNP6003 Module 3 usually ask for?
The third PNP6003 module usually asks you to define performance measures for your organization and use data to make or justify a decision, showing how evidence shaped the choice.
How many performance measures should a nonprofit track?
A small set, often five to ten, each tied to a goal, with a clear definition, data source and reporting frequency. Too many measures dilute attention.
What is a weighted decision matrix?
A table that scores each option against agreed criteria, multiplies each score by the criterion's weight and adds the results, making the reasoning behind a choice visible.
Where can I find a free PNP 6003 Module 3 sample paper?
On this page: a free clinic sets five measures, pilots them and uses the results and a weighted matrix to choose how to spend a $90,000 grant, with the choice tested under other weights.
What if my organization has no data yet?
Collect a small amount quickly, as a short pilot, and treat it as an indicator. A decision informed by limited data and stated cautions is stronger than one based on impressions.