Course Overview

Quantitative methods play an important role in answering questions about program impact, effectiveness, and fidelity of implementation. This introductory course is designed for professionals who want to build a foundational understanding of quantitative research meQuantitative methods help professionals answer questions about program impact, effectiveness, and fidelity of implementation. In this introductory course, participants will build a foundational understanding of quantitative research methods commonly used in evaluation and applied research.

Participants will explore experimental and quasi-experimental designs, examine how evaluators use quantitative data to answer key questions, and learn how analytic techniques support different research designs. The course also addresses measurement, surveys, databases, and ethical considerations. Throughout the sessions, real-world examples will show how practitioners apply these methods in practice.

What You Will Learn

By the end of this course, participants will be able to:

• Explain how quantitative methods support evaluation and applied research
• Compare experimental and quasi-experimental designs and determine when to use each
• Identify common validity threats and ethical considerations in quantitative studies
• Describe key concepts related to sampling, measurement, and data sources
• Interpret foundational analytic techniques used in quantitative analysis
• Link research design decisions to appropriate analytic approaches

Course Format

This course is delivered in four live, online, interactive modules (or two-days in person). Each session combines presentation, discussion, and applied examples. The course targets participants who are new to quantitative methods. Participants who complete all four modules will receive a certificate of completion.

Module Breakdown

Module 1: Experimental and Quasi-Experimental Designs, Part I

In the first module, participants examine the foundations of quantitative research design. The session introduces random and non-random designs and explains when each design works best. Participants also explore common threats to validity and consider ethical issues that arise when applying experimental and quasi-experimental approaches.

Module 2: Experimental and Quasi-Experimental Designs, Part II

This module builds on the first session by focusing on practical application. Participants analyze real-world examples that demonstrate how evaluators implement experimental and quasi-experimental designs. Through these examples, participants assess the strengths and challenges associated with different design choices.

Module 3: Measurement and Databases

This module focuses on the data that underpin quantitative designs. Participants will explore different types of quantitative data, issues related to sampling and measurement design, and strategies for operationalizing constructs. The session also addresses the use of existing measures and databases to answer evaluation questions.

Module 4: Introduction to Analytic Techniques

The final module introduces commonly used statistical concepts at a conceptual level. Topics include descriptive and inferential statistics, such as regression, t-tests, and analysis of variance. Participants will learn how analytic techniques connect to research designs and how to approach the analysis process without requiring prior statistical training. The instructor will also share resources related to analysis software and opportunities for further learning.

Who Should Attend

This course is ideal for:

• Professionals who are new to quantitative research or evaluation
• Evaluators and researchers seeking a stronger foundation in quantitative methods
• Program staff who work with quantitative data or evaluation findings
• Anyone interested in understanding how quantitative evidence is generated and used

Prerequisites

No prior experience with quantitative methods or statistics is required. All course materials will be provided.