Some Methods for the Analysis of Event Sequence Data from Multiple Respondents

Abstract

The analysis of sequence data poses a great challenge; existing methods for the comparison of sequences take what theoretical grounding they have from other fields (most importantly, genetics). We argue that set theory provides a way of establishing the relations between sequences that has a natural application to the case in which the sequences are orderings of a set of events, all compatible with an underlying partial order shared by multiple respondents. We also examine the possibility that observed differences in sequence data come from a stochastic process in which some events are poorly ordered because respondents cannot discriminate between them. We show that such methods shed light on several quantities that have been of interest to researchers, such as the degree of consensus within a group as to ideal sequences, the degree of respondent accuracy in reporting, and the degree to which plans and reality coincide.