Paul R. Yarnold
Optimal Data Analysis, LLC
Often at the advice of their physician, patients managing chronic disease such as fibromyalgia or arthritis, or those undergoing therapy in rehabilitation medicine or oncology, will record weekly or daily—sometimes even real-time ratings—of physical (e.g., pain, fatigue) and emotional (e.g., depression, anxiety) symptoms. Similarly, often at the advice of their coaches, athletes ranging from elite professionals to everyday people engrossed in an astonishing variety of group and customized personal fitness programs, record ratings of physical (e.g., vigor, focus) and emotional (e.g., anger, fear) states at the beginning and/or the end of workout sessions. In these types of applications, within a given study (also typically in most related studies in any given discipline, and across different disciplines), physical and emotional symptoms and states are all assessed using the same type of measuring scale, specifically an ordered Likert-type scale with 3-11 response categories. This paper shows how to employ UniODA to compare such symptom or state ratings to identify the strongest and weakest symptoms/states using data obtained across multiple measurements for an individual.