Ridderprint

Expanding the methodological toolbox of HRM researchers 53 (R Core Team, 2016) as well, using the lavaan package (Rosseel, 2012), but this requires some technical expertise. Similar to SEM, latent bathtub models can be extended to include multiple variables: both continuous and discrete latent variables, with single or with multiple response variables at the micro-level (Bennink et al., 2015). More detailed statistical descriptions of the model can be found in Croon and Van Veldhoven (2007) and Bennink and colleagues (2013, 2014) whereas a syntax for bathtub implementation in Mplus is provided by Bennink (2014). 3.3 Optimal Matching Analysis and Employee Engagement The rapid development of HRM technology has initiated a trend towards the continuous measurement of personnel behaviors and cognitions. Mobile applications, social networks, sociometric badges, wearables, and continuous employee feedback systems are rendering more complex and longitudinal HRM data (Angrave et al., 2016; Bersin, 2015). Employee engagement is one of the constructs that organizations increasingly measure on such an ongoing basis, potentially because research demonstrates it is less stable than previously assumed. Although the work engagement employees experience is often regarded as a stable state of mind, with a dispositional element to it (Macey & Schneider, 2008; Schaufeli, Salanova, González-Romá, & Bakker, 2002), studies demonstrate that the explained variance among consecutive yearly measures ranges from a high 74% to a low 31% (Mauno, Kinnunen, & Ruokolainen, 2007; Schaufeli, Bakker, & Salanova, 2006; Seppälä et al., 2009). It seems that there is also a temporary, transient element to engagement, as employees report weekly and even daily fluctuations (Bakker & Bal, 2010; Llorens, Schaufeli, Bakker, & Salanova, 2007; Sonnentag, 2003). In line with the above, researchers call for prolonged periods of observation with more frequent measurements of engagement. For example, Harter and colleagues (2002) conclude their meta-analysis of the Gallup engagement data calling for “ longitudinal designs that study changes in employee satisfaction-engagement, the causes of such changes, and the resulting usefulness to the business future research ” (p.276). Similarly, despite their relatively stable results, Seppälä and colleagues (2009) argue that “ longer follow-up with several measurement points would also allow investigation of the developmental trajectories of work engagement; utilizing a person-oriented approach would yield a more specific understanding of stability/change in work engagement than the conventional methods of the variable-centered approach ” (p.478). HRM researchers have been using methodology other than GLM to examine longitudinal patterns for quite some time (Sanders et al., 2014), but optimal matching analysis (OMA) remains relatively unknown in the field. OMA is a quantitative method originating from the natural sciences, where it has been especially useful in detecting temporal patterns. The method works by assessing the similarity among longitudinal sequences, after which a user-specified unsupervised learning algorithm can be used to group the sequences based on their similarity. The result is a categorical variable representing the patterns hidden in the longitudinal data, which can be an insightful classifier on its own or can function as a predictor or outcome variable in further analysis.