A key obstacle to measurement is the aggregation problem. Where indicators tap into common latent traits in theoretically meaningful ways, the problem may be solved by applying a data-informed ("inductive") measurement model, for example, factor analysis, structural equation models, or item response theory. Where they do not, researchers solve the aggregation problem by appeal to concept-driven ("deductive") criteria, that is, aggregation schemes that do not presume patterns of covariance across observable indicators. This article introduces a novel approach to scale construction that builds on the properties of concepts to solve the aggregation problem. This is accomplished by regarding conceptual attributes as necessary-and-sufficient conditions arrayed in an ordinal scale. While different sorts of scales are useful for different purposes, we argue that "lexical" scales are in many cases superior for research questions where it is relevant to combine the differentiation of an ordinal scale with the distinct, meaningful categories of a typology.