A fundamental tenet of meta-analysis is that effect sizes (EFSs) based on scores from different measures are directly comparable. Two recent models of EFS comparability have posited 2 measurement conditions necessary for EFSs based on scores from different measures to be comparable. This article addresses a number of problems with these models. First, a conceptual basis in representational measurement theory is developed for the conjunction of these measurement conditions. In addition, the discussion shows that the conjunction of these measurement conditions implies the scores from the different measures are linearly equatable. I include a definition and explication of a form of EFS comparability that is important for meta-analysis. Then, proofs are given regarding the comparability of correlation and of standardized mean difference EFSs based on scores from different measures. The article proposes a method for assessing the plausibility that the conjunction of these measurement conditions holds in a population of interest, filling a major gap in prior treatments of EFS comparability. The article concludes with consideration of the implications of the model of EFS comparability that is explicated for the use of meta-analysis in social work research.