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Expanding the methodological toolbox of HRM researchers 59 employees display in their weekly schedules. Using the data collected by sociometric badges and ‘ smart ’ workplaces, HRM scholars and practitioners could use OMA to uncover patterns in the use of office spaces over time or across locations. This may have direct practical value in terms of the cost reductions related to facility management, but may also be insightful for the design of flexible work arrangements. Moreover, certain configurations of work schedules within teams may have a detrimental impact on their effectiveness. This could be examined by using the cluster output of OMA in subsequent bathtub models, raising the work schedules to a team-level and relating them to team performance. Furthermore, talent management research may benefit from OMA and bathtub models. Contemporary organizations often focus their attention on a small group of employees labeled with high leadership potential (so-called “HYPO’s”), consistent with the literature on Paretian performance distribution (O’Boyle & Aguinis, 2012). OMA could model the history of job positions that distinguish such HYPO’s from other employees. Moreover, one could examine whether receiving the status of HYPO influences the developmental opportunities employees get in the period that follows. The characteristics of the clusters derived by an application of OMA to such research questions can be directly valuable for the design of talent management policies and practices. Moreover, the cluster output may function as input data for further analysis examining the causes and results of cluster membership. With or without the cluster information, bathtub models could examine how talent management policies affect individual employees and, in turn, organizational performance. Moreover, bathtub models could examine which HRM practices stimulate the development of employees in general, and HYPO’s in specific, and whether this development contributes to the achievement of business goals. Finally, irrespective of HRM implementation, a latent variable model could be used to examine whether the presence of HYPO’s in a team influences team effectiveness. Finally, the flexibility of latent variable models makes it a valuable tool to examine a wide variety of contemporary HRM themes. For example, latent approaches can also be used to model unobserved heterogeneity between respondents. This can be valuable to HRM research on team diversity, for example, in terms of location, tenure, age, gender, or cultural background. A more abstract example lies in the investigation of team heterogeneity in terms of individual psychological contracts (e.g., Bakk, Tekle, & Vermunt, 2013). Similarly, the data of e-mail traffic or sociometric badges can be used to examine heterogeneity in terms of the personal networks employees have. Subsequently, the impact of such heterogeneity on the development, retention, or performance of individuals, teams, and organizations could be assessed. Combined with OMA, this latent heterogeneity of teams could be monitored over longer periods of time to see whether improvement occurs, potentially as a result of changes in HRM policies. Moreover, latent variable models can be used to grasp constructs that are otherwise hard to measure. For instance, a latent performance score could be estimated using multiple indicators of employee’s behavior or job output. This latent score has the potential to be a more accurately reflection of employees’ actual performance than the separate indicators or their combined average (Murphy, 2008).