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Rasch Scaling and Cumulative Theory-Building in Consciousness Research

This paper illustrates how Rasch measurement can be applied to consciousness research and psychological and medical research in general, and more generally, by highlighting the qualitative and the quantitative information provided by this approach by means of graphs. Rasch models specify the conditions under which measurement can occur—they are inherently prescriptive rather than descriptive—and poor model fit calls into question the data rather than the model. The partial credit model is presented to illustrate several central issues, for example, although raw sum scores are sufficient statistics, they are not measures; estimation errors always increase for more extreme raw scores; items’ estimated properties do not vary with the persons used for their calibration; person measurement does not depend on the particular items being used. These properties support cumulative theory building through item banks as well as computer-adaptive testing. In practice, models always fit imperfectly and item bias and indices of measurement noise (outfit) are introduced to capture such misfit. The article focuses on item maps in the context of near-death experiences, the Revised Transliminality Scale, and the University of Pennsylvania Smell Identification Test olfactory test. I show how item maps guide a construct’s qualitative interpretation, how they can visually capture the quality of an instrument, and serve as an engineering tool for test construction. Finally, I outline how standard item calibration techniques generalize to a dynamic approach suitable for modern online Big Data contexts.