This case study outlines the use of multilevel modeling for meta-analysis to analyze the meta-analytic data for a 3-year project. The focus of the project, which was funded by the National Science Foundation, was on comparing the performance (achievement) of undergraduate students who were taught using one of the various types of active small-group learning methods with their counterparts who were taught by the traditional lecture-based instruction in Science, Technology, Engineering, and Mathematics college classrooms. The meta-analytic data consisted of the small-group learning effect sizes, sampling variances of the effect sizes, and the coded characteristics of the primary studies included in the meta-analytic review. Specifically, the main objectives of the project were (a) surveying the Science, Technology, Engineering, and Mathematics education literature to locate and identify the pedagogical primary research studies that had focused on the effectiveness of the small-group learning methods in undergraduate Science, Technology, Engineering, and Mathematics college classrooms; (b) aggregating and synthesizing the small-group learning effectiveness primary studies, which were included in the meta-analytic review (the primary studies were selected based on preset inclusion/exclusion criteria); and (c) using the multilevel modeling methods for meta-analysis to provide more accurate and precise estimates as well as generalizable results when compared with the traditional fixed-effects meta-analysis methods. [This content is provided in the format of an e-book.]