Investigating Sequences in Ordinal Data: A New Approach with Adapted Evolutionary Models

Abstract

This paper presents a new approach for studying sequences across combinations of binary and ordinal variables. The approach involves three novel methodologies (frequency analysis, graphical mapping of changes between “events”, and dependency analysis), as well as an established adaptation based on Bayesian dynamical systems. The frequency analysis and graphical approach work by counting and mapping changes in two variables and then determining which variable, if any, more often has a higher value than the other during transitions. The general reasoning is that when transitioning from low values to high, if one variable commonly assumes higher values before the other, this variable is interpreted to be generally preceding the other while moving upwards. A similar reasoning is applied for decreasing variable values. These approaches assume that the two variables are correlated and change along a comparable scale. The dependency analysis investigates what values of one variable are prerequisites for values in another. We also include an established Bayesian approach that models changes from one event combination to another. We illustrate the proposed methodological bundle by analyzing changes driving electoral democracy using the new V-Dem dataset (Coppedge et al. 2015a, b). Our results indicate that changes in electoral democracy are preceded by changes in freedom of expression and access to alternative sources of information.