Data Decisiveness, Missing Entries, and the DD Index
Jan E. De Laet, ERIK F. SMETS · Cladistics · 1999
The decisiveness of a data set has been defined as the degree to which all possible dichotomous trees for that data set differ in length, and the DD statistic (the data decisiveness index) has been proposed to measure this degree. In this paper, we first discuss an exact nonre cursive formula for the length of indecisive datasets (DD = 0) that consist of informative binary characters in which no missing entries are allowed. Next, the concept of indecisive data sets is extended to data sets in which missing entries may be present. Last, indecisive data sets with missing entries are used as an aid to construct hypothetical data sets that single out some of the factors that influence the DD statistic. On the basis of these examples, it is concluded that the concept of data decisiveness is too elusive to be captured into a single and simple index such as DD.