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

• We examine result quality of optimal matching analyses for career data with different properties.
• For N > 50, sample size does not substantially improve result quality of optimal matching analyses.
• Sequence length is the most relevant factor for result quality.
• Sequences with up to 30% missing elements can be used for optimal matching analysis.

Optimal matching is a method for the analysis of sequential data. It allows researchers to detect patterns in career sequences or occupational trajectories. We first give a brief introduction to the method and review the existing career literature that employs optimal matching. To examine which data properties are required for optimal matching analysis, we conducted Monte Carlo simulations of career sequences with varying parameters for sequence length, sample size and missing items. We find that sequence length is the relevant factor for correct results, while sample size does not substantially affect result quality. Another important finding is that sequences with up to 30% elements missing can be used for optimal matching analysis. We also show which settings for the optimal matching procedure deliver the best results.