Date de l'événement : 2026-11-02 The Evaluators' Institute Date limite : 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.
Strategic planning is becoming a common practice for governments, nonprofit organizations, businesses, and collaborations. The severe stresses facing these entities make strategic planning more important and necessary than ever. For strategic planning to be really effective it should include systematic learning informed by evaluation. This course examines the theory and practice of strategic planning and management with an emphasis on practical approaches to identifying and effectively addressing organizational challenges – and doing so in a way that makes systematic learning and evaluation possible. The approach engages evaluators much earlier in the process of organizational and programmatic design and change than is usual. Topics covered include understanding why strategic planning has become so important; understanding what strategic planning is – and is not; the Strategy Change Cycle; key strategic planning tools and techniques; and designing formative, summative, and developmental evaluations of strategic planning processes, missions, strategies, and organizational performance.
Date de l'événement : 2026-11-04 The Evaluators' Institute Date limite : 2026-11-02
This course will introduce a range of basic quantitative and qualitative social science research methods that apply to evaluating various programs. This foundational course introduces methods developed more fully in other TEI courses and serves as a critical course designed to ensure a basic familiarity with a range of social science research methods and concepts. Topics will include qualitative research with a special emphasis on focus groups and interviews, experimental design, quasi-experimental design, and survey research methods. This course is suitable for those who want to update their existing knowledge and skills and will serve as an introduction for those new to the topic.
Evaluators are frequently in evaluation situations in which they are collecting data through multiple methods, often both qualitative and quantitative. Too often, however, these study components are conducted and reported independently, and do not maximize the explanation building that can occur through their integration. The purpose of this course is to sensitize evaluators to the opportunities in their work for designing and implementing mixed methods, and to be more intentional in the ways that they design and implement their studies to integrate qualitative and quantitative approaches. This highly interactive course will begin with an overview of the issues involved with mixed-methods research, then focus on the research questions and evaluation situations that are conducive for mixed-methods, and the variety of designs that are possible. A significant portion of the course will be on strategies for implementing mixed-methods designs and analyzing the data to maximize their integration.