In recent years, quantitative research methodology has become more conceptually integrated and technically sophisticated. Fundamental insights regarding design and analytic frameworks that support causal inference along with the development of estimation algorithms appropriate for multilevel and latent variable models have altered traditional methodological practice and ushered in new appreciation for the underlying relationship among modern data modeling techniques. In this article, I provide a brief outline of five methodological content domains that have increasing relevance for quantitatively oriented evaluators, (1) causal inference/experimental design, (2) multilevel modeling, (3) structural equation/latent variable modeling, (4) longitudinal data analysis, and (5) missing data, and the accompanying textbook resources that facilitate understanding and use. A target audience for each text is also identified.