Event Date: 2026-10-28 The Evaluators' Institute Deadline: 2026-10-26
This course provides public health practitioners with a practical, skills-based introduction to program evaluation using the CDC’s Framework for Program Evaluation in Public Health as a guiding structure. Participants will learn how to design and implement high-quality evaluations that improve program effectiveness, strengthen decision-making, and support accountability to stakeholders. Through simulated scenarios, learners will practice applying core evaluation competencies—such as developing logic models, engaging stakeholders, selecting appropriate methods, collecting and analyzing data, and communicating findings clearly. Each module integrates opportunities for hands-on application and individualized feedback, with an emphasis on translating evaluation concepts into practical tools that can be used immediately in public health settings. By the end of the course, participants will be able to apply evaluation skills confidently and strategically to enhance programs in their own professional contexts.
Event Date: 2026-10-29 SQEP Deadline: 2026-10-28
NVivo Training (Level 2): From Coding to Comparative and Thematic Analysis will help you deepen your use of NVivo to explore, compare, and interpret qualitative data. Topics will include:• Text search queries and advanced thematic analysis;• Coding comparison and content analysis;• The wizard for comparing groups (attributes);• Matrix coding queries and cross-tabulations;• Interpretation and visualization of results.
Event Date: 2026-10-30 The Evaluators' Institute Deadline: 2026-10-28
Navigating the intersection of artificial intelligence (AI) and evaluation practice presents new challenges and opportunities for evaluators. This course aims to demystify AI by demonstrating how emerging tools can be thoughtfully integrated throughout the evaluation process to strengthen practice, increase efficiency, support methodological rigor, and contribute to the meaningful use of evaluation. Participants will explore how AI can support key stages of evaluation while critically examining the ethical and practical implications of AI use in diverse contexts. Emphasis will be placed on maintaining evaluator judgment, protecting interest holder voice, and promoting equity in AI-assisted evaluations. Participants will use simulation-based software to apply AI tools in a realistic evaluation context. Working with program documents, interest holder information, and qualitative and quantitative data, participants will have opportunities to practice using AI across multiple stages of an evaluation while making decisions about the appropriateness and responsible use of these tools.
Event Date: 2026-11-02 The Evaluators' Institute Deadline: 2026-10-29
AI-powered tools are rapidly transforming how data is analyzed, interpreted, and communicated. This course provides an accessible introduction to leveraging AI throughout the evaluation process—from literature review, research design, and data analytics, to writing reports and preparing presentations. Our goal is to explore the capabilities of AI across evaluation; where it’s strong, and where it should be avoided. Topics include AI-driven data cleaning and exploration, automating statistical analysis, enhancing visualizations, and using AI for reporting. We will also cover best practices for evaluating AI-generated outputs and avoiding common pitfalls. The course is designed for professionals who are already comfortable with fundamental statistical concepts and are ready to explore how AI can support and extend their analytical capabilities. This course is ideal for applied researchers, evaluators, and data professionals who are familiar with basic statistical analysis and are looking to incorporate AI tools into their workflow.
Most evaluation data are more complex than standard statistical methods assume. The constructs we care about cannot be observed directly and are measured imperfectly through surveys and instruments. At the same time, participants are nested within sites, sites within regions, and measures are often repeated over time. When evaluators apply conventional approaches to data like these, the result is often biased estimates, misleading conclusions, and findings that don’t hold up to scrutiny. This three-day course introduces structural equation modeling (SEM), including confirmatory factor analysis (CFA) and multilevel modeling (MLM). The course is organized around problems that evaluators regularly encounter, with methods introduced as tools to address them. Each day builds on the last, moving from measurement to data structure to their integration. Lectures and discussions are paired with applied software demonstrations throughout the course. Participants are encouraged to bring their own data for use during Day 3 application sessions.