Embracing a Cluster Perspective: Optimal Matching Analysis Tutorial and Tool Demonstration

This paper presents a tutorial on Optimal Matching Analysis (OMA). OMA is a computational approach that focuses on processes which compare sequences of states of and among observations in a sample. It has various advantages, from analyzing multiple waves of data as a whole unit of analysis, to rigorously handling censored or truncated data. This paper introduces OMA, illustrated by a 5-step tutorial on how to prepare data for, conduct and apply this technique. This tutorial replicates parts of Setor and Joseph's paper to illustrate the application and interpretation of OMA using updated cohort data from the National Longitudinal Survey of Youth 1997 (NLSY97). OMA applied to the NLSY97 reveals 6 clusters of residential patterns with demographic (e.g., sex and ethnicity), and human capital (e.g., education and income) descriptions. This tutorial also shares the analysis of multiple sets of sequences (etc., residential and career). Overall, this paper hopes to encourage OMA as a viable approach in IS research and equip researchers with an understanding of how OMA works. This approach would be useful for researchers, especially those who routinely track and work with repository data and those who intend to look at their data in a different light.