Further Reading — Chapter 10: The Evaluation Plan

Verify current funder requirements; the best evaluation plans match the funder's expectations.

Logic Models and Theory of Change

  • W.K. Kellogg Foundation, Logic Model Development Guide (free PDF). The classic, widely used primer — inputs through impact, with clear examples and templates. The essential reference for Section 10.2.
  • University of Wisconsin–Extension, "Enhancing Program Performance with Logic Models" (free course). A practical, well-regarded guide and templates used across the nonprofit and program-evaluation worlds.
  • Center for Theory of Change (theoryofchange.org). Free resources for articulating the assumptions under the logic model's arrows.

Program Evaluation Method

  • CDC, "Framework for Program Evaluation in Public Health" and the CDC Evaluation resources. A clear, authoritative framework for process and outcome evaluation, indicators, and data collection — applicable well beyond public health.
  • Patton, Michael Quinn. Utilization-Focused Evaluation and Developmental Evaluation. The standard works on making evaluation useful and on adaptive/formative evaluation (Section 10.6).
  • Rossi, Lipsey, and Henry. Evaluation: A Systematic Approach. A comprehensive textbook on program evaluation for those who want depth on design, measurement, and analysis.

SMART Objectives and Indicators

  • Funder and agency guides to SMART objectives and performance measurement (e.g., from SAMHSA, HRSA, the Department of Education). Many publish specific guidance and required performance measures — read your target funder's.
  • The Aspen Institute, Urban Institute, and Mathematica resources on outcome measurement. Practical guidance on choosing valid, feasible indicators and avoiding vanity metrics.

Research Analysis Plans and Power

  • NIH "Rigor and Reproducibility" and statistical/sample-size guidance (grants.nih.gov). Official expectations for the analysis plan and power justification in research proposals.
  • A statistics or study-design text (e.g., Rosner, Fundamentals of Biostatistics), the power-analysis chapters; and tools like G*Power. For computing and justifying sample size. If you're not a statistician, involve one early — it's the research analogue of the external evaluator.