Optimal matching analysis in career research: A review and some best-practice recommendations

Optimal matching is a method for the analysis of sequential data. It allows researchers to detect patterns in career sequences or in trajectories of vocational development. After giving a brief introduction to the method, we review the present literature on careers and vocational development to show where optimal matching analysis has already been employed. We then conduct Monte Carlo simulations of data with varying parameters for sequence length and sample size. Based on the results from these simulation studies, we recommend which properties data sets should have for an optimal matching analysis. We also provide guidelines on how to code sequences, discuss how to deal with missing values, and show which cost settings and clustering algorithms deliver the best results for data sets with specific properties