In any behavioral intervention study including development to trial-type studies, decisions must be made about the type of outcome data that will be gathered and the instruments, measures, and methods that will be used for data capture. Decisions must also be made about other types of measures such as screening measures or measures that might be used to characterize a sample or serve as mediating variables. Measurement decisions are a critical aspect of behavioral intervention research and important at all phases of the pipeline. As discussed in this chapter, there are a myriad of measures available and a variety of ways to gather outcome data. The choice of measures must be guided by relevant theories and models, the current literature, the research questions of interest and hypotheses, the interests of stakeholders, characteristics of the target population, and the stage in the intervention pipeline. There are also a number of practical considerations such as cost, convenience, and participant burden. There is no simple answer to the question: Which measures should I use? Equally important is being able to comprehend, integrate, and interpret data that is yielded from the measures chosen. It is also important to recognize that measurement is not perfect and all measures are subject to error. An error can be random, or caused by chance, or be systematic resulting from the measure itself (e.g., confusing questions), method of administration (e.g., untrained assessors), or environmental influences (e.g., noise). It is important to be aware of the sources of measurement errors so that steps can be taken to minimize them such as pilot testing measures and training assessors.