Sequence analysis and optimal matching are useful heuristic tools for descriptive analysis of heterogeneous individual pathways such as educational careers, job sequences or patterns of family formation. To date, it remains unclear though how to handle the inevitable problems posed by missing values with regard to such analysis. Multiple Imputation (MI) offers a possible solution for this problem, but has hardly been tested in the context of sequence analysis.
Against this background this contribution assesses the potential of MI in the context of sequence analyses on the basis of an empirical example. Methodologically, we draw upon corresponding work of Brendan Halpin, and extend it to further types of missing value patterns. Our exemplary empirical case is a sequence analysis of panel data with substantial attrition. It examines the typical patterns and the persistence of sex segregation in school-to-work transitions in Switzerland.
Preliminary results indicate that MI proves of value to handle missing values due to panel mortality in the context of sequence analysis. MI especially facilitates a sound interpretation of the resulting sequence types.