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Expanding the methodological toolbox of HRM researchers 47 3.1 Introduction Human resource management (HRM) emerged as a function in the early 20 th century to effectively manage and rationalize the employment relationship (Ulrich & Dulebohn, 2015). Nowadays, HRM is increasingly becoming a ‘science’ that aims to enhance the decisions organizations make regarding their human capital (Boudreau & Ramstad, 2005; 2007; Rasmussen & Ulrich, 2015; Ulrich & Dulebohn, 2015). In creating a basis of evidence for such decisions, HRM scholars have primarily relied on general linear models (GLM) such as linear regression. However, the data gathered and compiled in the contemporary HRM function is increasingly of hierarchical and longitudinal nature, causing the current methodological toolbox of HRM researchers to fall short (Angrave et al., 2016; Bersin, 2015). Methods other than GLM may better account for the complex effects in these new forms of HRM data. On the one hand, organizational entities are hierarchical structures which causes the effects of HRM to occur at and across different levels of analysis simultaneously (Hitt, Beamish, Jackson, & Mathieu, 2007; Wright & Nishii, 2007). On the other hand, as measurement happens on a more continuous basis, HRM data structures often consist of many observations nested within subjects over prolonged periods of time (Angrave et al., 2016; Bersin, 2015). Acknowledging the above, scholars have been increasingly moving from GLM applied at a single level of analysis towards multi-level techniques (Boselie, Dietz, & Boon, 2005; Sanders, Cogin, & Bainbridge, 2014; Snape & Redman, 2010). However, the most commonly applied multi-level methods do not work well when examining bottom-up effects, linking individual phenomena to organizational- level outcomes, and they can become overly complex when examining multiple, potentially categorical, variables simultaneously and over prolonged periods of time. This article proposes two statistical methods that are rarely applied to HRM research questions, despite having added value over and above more traditional methodology. First, bathtub models are proposed as a way to account for multi-level models where the outcome resides at the higher level of analysis. Outperforming traditional aggregation and disaggregation approaches (Bennink, 2014), bathtub modeling can add value to HRM research on, among others, group composition or bottom- up effects. Second, optimal matching analysis (OMA) is advocated for its ability to detect longitudinal patterns. 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 paper aims to demonstrate the added value of each method to the HRM methodological toolbox by discussing their applicability to research on employee engagement. After discussing each method’s strengths and weaknesses separately, the paper concludes with overview of potential future applications and synergies. 3.2 Bathtub Modeling and Engagement Over the past two decades, the influence of HRM on organizational performance has received much scholarly attention (e.g., Becker & Gerhart, 1996; Paauwe, Guest, &Wright, 2013) and a major part of the impact of HRM policies and practices has been demonstrated to be indirect via the behavior of employees (Christian, Garza, & Slaughter,