There is often confusion about distinctions between growth models and value-added models. The first half of this paper attempts to dispel some of these confusions by clarifying terminology and illustrating by example how the results from a large-scale assessment can and will be used to make inferences about student growth and the value-added attributable to teachers or schools. Two key differences between growth models and value-added models are discussed: the unit of analysis (growth models focus first and foremost on students; value-added models focus on teachers or schools) and inferential intent (growth models are primarily descriptive, value-added models are meant to support causal inferences). The point is made that all growth models can be used to make value-added inferences, but value-added models almost never lead to student-specific inferences about growth. The focus of the second half of this paper is on design issues that will need to be considered by the Partnership for Assessment of Readiness for College and Careers (PARCC) consortium such that test scores can be used for either growth or value-added inferences. It is shown that vertically scaled test scores are "not" prerequisite for value-added modeling. Vertical scales are most desirable in support of student-level growth interpretations, but certain conditions must be met before their creation would be defensible. In particular, the case is made that vertical scales will be most compatible with a learning progression perspective on construct and item development. The second half of the paper also discusses design factors that would minimize the role that measurement error will play in distorting inferences about growth and value-added. Finally, the paper concludes with the some recommendations for the PARCC consortia.