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Chapter 3 58 techniques that, despite their novelty to the field, can be valuable additions to the methodological toolbox of HRM researchers and practitioners. Particularly in light of the growing need to justify, prioritize, and improve decision-making (Boudreau & Ramstad, 2007; Rasmussen & Ulrich, 2015; Ulrich & Dulebohn, 2015) and the new forms of HRM data that arise due to technological developments (Angrave et al., 2016; Bersin, 2015). Using latent variables, bathtub models are put forward as the solution to examine multi- level mechanisms with outcomes at the team or organizational level without decreasing the sample size or neglecting the variation inherent in employees’ responses to HRM activities. Optimal matching analysis is proposed as particularly useful to examine the longitudinal patterns that occur in repeated observations over a prolonged timeframe. Research on employee engagement was used to illustrate how each method functions and how they add value over and above the current methods used in HRM research. Although both bathtub modeling and OMA both elevate micro-level data to a macro-level, the two methods strongly vary in their purpose, in their complexity, and in the expertise required to implement them. The application of OMA does not require deep statistical or conceptual knowledge and the pattern visualizations facilitate an easy interpretation. However, this simplicity is also reflected in the primarily descriptive insights the method provides. In contrast, the underlying equations as well as the output of bathtub models may be harder to explain to laymen such as business and HRM professionals (see Bennink et al., 2013, 2014; Croon & Van Veldhoven, 2007). This increase the difficulty that scholars and HRM analytics professionals may experience in translating the latent variable model’s results into actionable insights for decision- makers. Nevertheless, both techniques can add value to HRM research on a variety of themes, either applied separately or in synergy with each other and other methods. Recruitment and selection is one field of potential future application. OMA has been frequently applied on career patterns (e.g., Blair-Loy, 1999) and, similarly, by clustering applicants based on their prior work experiences, the method could be valuable for selection purposes. For example, applicants’ historic job positions can be coded into unique states based on the associated management responsibilities or the required level of technical expertise. The required 25 months of input data for OMA (Dhouly & Biemann, 2015) could be extracted directly from applicants’ résumés, but the digital job market becomes an ever-richer data source as well (e.g., LinkedIn, Xing, ResearchGate). The resulting clusters may facilitate decision-making in the selection process, similar to applicants’ assessment center scores, interviewer ratings, and other recruitment data. Additionally, a latent bathtub model could use such data to examine the effectiveness of recruitment, selection and/or socialization practices. Irrespective of these practices, bathtub models could also investigate whether certain (combinations of) applicant profiles improve the effectiveness of teams. The methods may additionally be valuable with regard to workforce planning, facility management, and flexible working arrangements. Recent work by Lesnard and Kan (2011) demonstrates how a two-stage OMA can be used to first cluster the daily work schedules of employees, and subsequently use these clusters to unveil the patterns