This chapter introduces three important applications of Rasch measurement principles and techniques to solve the sorts of problems that many of us encounter in our human sciences research. To address these issues, we have invited three of our colleagues to share their expertise directly with you. The first author, Yan Zi, from the Hong Kong Institute of Education, created a Rasch interval-level measurement scale from a large data bank of fitness indicators routinely used in Hong Kong primary schools. Yan’s contribution to this chapter demonstrates the use of Rasch diagnostics to decide which indicators/items to include or exclude in constructing the Rasch Measurement Physical Fitness Scale (RMPFS). The second author, Gregory Stone from the University of Toledo, explains the rationale and demonstrates practices for objective standard setting for test data and for judged performances using raters. Stone explains how to use Rasch measurement to set cut points for setting standards for high-stakes decision making. The third author, Svetlana Beltyukova from the University of Toledo, uses Rasch measurement to extract a set of airline attributes to predict overall passenger satisfaction with airline performance. For this task, she introduces the Rasch regression model that Ben Wright pioneered in 2000.