Navigate complex organizational decisions through leadership approaches that systematically integrate data analysis with authentic community engagement. Grounded in practitioner-tested frameworks and transformative leadership theory, this course builds capacity for strategic decision-making that moves beyond compliance-focused, hierarchical models toward evidence-informed, equity-centered choices. Participants develop competency in problem classification, statistical interpretation, participatory design, and systematic decision frameworks while building a working decision-support system for their own organization. Designed for educational leaders, healthcare managers, nonprofit executives, and public service professionals who must balance rigorous data analysis with inclusive stakeholder engagement in complex, high-stakes decisions. All analytical content is taught at the practitioner level; no prior statistics coursework required.
Number of Units: 3.0 graduate level extension credit(s) in semester hours Who Should Attend: This course is designed for leaders and decision-makers across education, healthcare, nonprofit, and government organizations who are responsible for navigating complex, high-impact organizational decisions. Required Materials:
Snowden, D. & Boone, M. (2007). A leader's framework for decision making. Harvard Business Review, 85(11), 68–76. [~9 pages; university library]
Safir, S. & Dugan, J. (2021). Street Data: A Next-Generation Model for Equity, Pedagogy, and School Transformation. Thousand Oaks, CA: Corwin Press. Chapters 2–3, 8 [~40 pages; instructor-provided excerpt, fair use; university library]
Powell, J., Menendian, S., & Ake, W. (2019). Targeted Universalism: Policy & Practice. Berkeley, CA: Haas Institute for a Fair and Inclusive Society. [28 pages
Arnstein, S. (1969). A ladder of citizen participation. Journal of the American Institute of Planners, 35(4), 216–224.
Anaissie, T., Cary, V., Clifford, D., Malarkey, T. & Wise, S. (2021). Liberatory Design [Card deck]. National Equity Project & Stanford d.school.
Instructor-created course materials: Decision Classification Guide, Community Engagement Planning Protocol, Decision Matrix Framework, Structured Practitioner Template Library [Original materials; CC BY-NC-SA 4.0; embedded in Canvas] → Modules 1–7
Prerequisites:
No specific coursework required. Participants should have professional experience in education, healthcare, nonprofit management, public administration, or a related public service field. Access to a real organizational decision-making challenge for portfolio application is expected. All statistical content is introduced at the practitioner level; no prior statistics background required.
Classify complex organizational problems using established frameworks (Cynefin, systems thinking) to select appropriate decision-making approaches and avoid mismatched solutions
Conduct rigorous data analysis for decision-making contexts, including statistical interpretation, bias identification, and multi-source data integration with attention to equity implications
Design and facilitate authentic community engagement processes using established participation frameworks (IAP2, Arnstein, Targeted Universalism) that center marginalized voices and integrate community expertise with technical data
Apply systematic decision-making frameworks that combine analytical rigor with participatory processes to address complex organizational challenges across sectors
Create decision-support systems — including monitoring protocols, feedback loops, and adaptation mechanisms — that improve long-term organizational decision-making capacity
Develop implementation and evaluation plans that translate decisions into specific, measurable actions with built-in learning and course-correction processes
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