Suppose an education researcher wants to test the impact of a high school drop-out prevention intervention in which at-risk students attend classes to receive intensive summer school instruction. The district will allow the researcher to randomly assign students to the treatment classes or to the control group. Half of the students (the treatment group) are assigned to one of four summer classes being offered. The other half (the control group) are not assigned to receive any services during the summer. Thus, the researcher knows there are four clusters in the treatment group: students in the same class share the same teacher and environment and, therefore, are expected to have more similar outcomes than students from different classes. The students in the control group, however, are not assigned to any classes. How are data for the treatment and control group students to be treated in the analysis? This scenario is an example of a Partially Nested Randomized Controlled Trial (PN-RCT) where treatment students receive intervention services in groups but where this grouping does not occur for control students. The purpose of this paper is to provide guidance to education researchers on how to recognize, design, and analyze data from PN-RCTs to rigorously assess whether an intervention (such as a curriculum, policy, or tutoring program) is effective. In the Introduction, the authors define PN-RCTs, with specific examples that are described in the broader context of the choices for research designs, and provide a roadmap to the rest of the paper and a summary of their take-away messages. Chapters 1 and 2 of the paper are written primarily for applied education researchers with an introductory knowledge of quantitative impact evaluation methods. The goal is to help these researchers negotiate key concerns when proposing and conducting research using PN-RCT designs. The paper addresses design issues such as possibilities for random assignment, cluster formation, statistical power, and confounding factors that may mask the contribution of the intervention. Chapter 3 is intended for education researchers interested in estimating treatment effects for PN-RCT designs; it discusses basic statistical models that adjust for the clustering of treatment students within intervention clusters, associated computer code for estimation, and a step-by-step guide, using examples, on how to estimate the models and interpret the output. Chapter 4 and the technical appendices discuss more advanced statistical topics pertaining to PN-RCTs and are written primarily for an audience with a strong statistical background. The following are appended: (1) Mixed Model Theory for PN-RCTs; (2) Degrees of Freedom for PN-RCTs; (3) Analyzing PN-RCT Data Using R Software; (4) Analyzing Basic PN-RCTs Using HLM Software; and (5) Full SAS Code for Examples.