Answering Ordinal Questions with Ordinal Data Using Ordinal Statistics

It is argued that ordinal statistical methods are often more appropriate than their more common counterparts for three types of reasons: Conclusions from them will be unaffected by monotonic transformation of the variables, they are statistically more robust when used appropriately, and they often correspond more closely to the goals of the investigator. Kendall's tau (Kendall, 1970) and its counterpart delta are recommended as having wide I applicability and good statistical behavior. It is recommended that they be estimated as population parameters and their standard errors estimated form the data. Ways in which they can then substitute for Pearson correlations and mean comparisons in a number of research contexts are suggested.