Paul R. Yarnold
Optimal Data Analysis, LLC
Imagine a single case is assessed on two or more binary attributes over a series of measurements or testing sessions. For example, a chronic pain patient might rate their pain and fatigue each evening before sleep, as “worse than average” or “better than average”. Alternatively, imagine following two or more different corporate stocks over a given time period, keeping a record at the end of every trading day regarding whether the closing price was higher or lower than on the prior day. Both examples are single-case series, and in both examples an interesting question is whether the binary responses to the different attributes differ. Does the patient have more bad pain days than bad fatigue days? Does one stock close higher more often than another stock? This article discusses how to accomplish such analysis and address such questions using UniODA. The example selected to illustrate the method provides clear evidence that ipsative standardization is critical in obtaining meaningful results in the analysis of serial data.