This overview of Rasch measurement models begins with a conceptualization of our continuous experiences that are often captured as discrete observations. It goes on to discuss the properties that are require of measures if they are to transcend the occasion in which they were collected, and concludes with a discussion the spiral of inferential development. This is followed by a discussion of the mathematical properties of the Rasch family of models that allow the transformation of discrete deterministic counts into continuous probabilistic abstractions on which science is based. The overview concludes with a discussion of six of the family of Rasch models, Binomial Trials, Poisson Counts, Rating Scale, Partial Credit, and Ranks and the types of data for which these models are appropriate.