Assessing the Potential of Multiple Imputation in Sequence Analysis

Imdorf, Christian; Sacchi, Stefan (9 October 2014). Assessing the Potential of Multiple Imputation in Sequence Analysis (Unpublished). In: Society for Longitudinal and Lifecourse Studies (SLLS) Conference - Lives in translation: life course research and social studies. Lausanne. 09.10.-11.10.2014.

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Sequence analysis and optimal matching are useful heuristic tools for the descriptive analysis of heterogeneous individual pathways such as educational careers, job sequences or patterns of family formation. However, to date it remains unclear how to handle the inevitable problems caused by missing values with regard to such analysis. Multiple Imputation (MI) offers a possible solution for this problem but it has not been tested in the context of sequence analysis. Against this background, we contribute to the literature by assessing the potential of MI in the context of sequence analyses using an empirical example. Methodologically, we draw upon the work of Brendan Halpin and extend it to additional types of missing value patterns. Our empirical case is a sequence analysis of panel data with substantial attrition that examines the typical patterns and the persistence of sex segregation in school-to-work transitions in Switzerland. The preliminary results indicate that MI is a valuable methodology for handling missing values due to panel mortality in the context of sequence analysis. MI is especially useful in facilitating a sound interpretation of the resulting sequence types.

Item Type:

Conference or Workshop Item (Paper)


03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Sociology

UniBE Contributor:

Sacchi, Stefan


300 Social sciences, sociology & anthropology
300 Social sciences, sociology & anthropology > 310 Statistics




Stefan Sacchi

Date Deposited:

30 Apr 2015 12:28

Last Modified:

30 Apr 2015 12:28

Additional Information:

Paper session Analytic Methods

Uncontrolled Keywords:

Multiple Imputation; Sequence Analysis; Missing Values; Optimal Matching


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