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

Expanding the methodological toolbox of HRM researchers: The added value of latent bathtub models and optimal matching analysis

“…Missing values are common in longitudinal research, but, compared to other methods, OMA handles them relatively easy. As long as no more than 30% of the elements within a sequence are missing, replacing missing values by an additional element (e.g., “X” or “?”) will result in a model performance that is nearly equal to that of a complete data set (3% decreased classification accuracy in Dlouhy & Biemann, ). Although late joiners, attrition, and other factors may cause sequences to have different lengths, OMA can handle these as long as the length of the shortest included sequence is at least 70% of that of the longest sequence.…”

“…OMA has several unique characteristics that make it an addition to the current methodological toolbox of HRM researchers. Similar to other longitudinal methods, OMA only requires a small sample and simulations demonstrate that results are still 95% accurate for samples as small as 50 employees (Dlouhy & Biemann, 2015). In contrast to other methods, however, OMA is a person-oriented method, meaning that the object under observation-the employee or teamis the focus of the analysis, rather than the variance in a specific independent variable (Abbot, 1988).…”

“…Additionally, OMA functions particularly well with the new forms of HR data. OMA's classification results only increase with the number of nested observations (Dlouhy & Biemann, 2015), while other longitudinal methods soon require higher order polynomials to model observations over prolonged periods of time (Curran et al, 2010). Finally, despite the many observations and dimensions potentially included in OMA, it remains a relatively easy method to implement and interpret.…”

“…It can reduce large volumes of both categorical and ordinal data into a smaller set of underlying trajectories. Although relatively unknown in the general HRM field, it has been a valuable tool for career pattern analysis (Dlouhy & Biemann, 2015). This article aims to demonstrate the added value of each method to the HRM methodological toolbox by discussing their applicability to research on employee engagement.…”

Student career path recommendation in engineering stream based on three‐dimensional model

“…Moreover, future research and needs for school counselor are further explored. In 2015, Doughy and Biemann [4] have reviewed the existing career literature that employs optimal matching. In optimal matching technique, sequential data are analyzed based on Monte Carlo simulations.…”