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Chapter 3 54 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 team – is the focus of the analysis, rather than the variance in a specific independent variable (Abbot, 1988). This allows OMA to examine patterns on multiple variables simultaneously, which is a more complex assignment for other longitudinal methods, such as multi-level and latent growth models (Curran, Obeidat, & Losardo, 2010). Hence, OMA be used to examine employees’ patterns on multiple dimensions of engagement (e.g., vigor, dedication and absorption; Schaufeli et al., 2002) or on engagement data coupled with other constructs. Additionally, OMA functions particularly well with the new forms of HRM data. OMA’s classification results only increases 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. To illustrate these advantages, the following section elaborates on a hypothetical study of weekly employee engagement data, like those gathered by a mobile application. The data requirements are described and afterwards each of the model steps is explained. Finally, several limitations and alternatives are discussed. 3.3.1 Data Requirements OMA works by comparing cases based on their temporal sequences. These sequences consist of a string of elements, each reflecting the temporal state of the case at a specific moment in time. OMA handles these elements as categorical labels at this stage of the analysis, not recognizing any ordinal nature among them. Although this seems a disadvantage, it allows OMA to discover patterns in data that does not necessarily have an underlying order. For example, employees’ trajectories across functions, locations, or organizational units (i.e., nominal or categorical variables) can be examined simultaneously with their engagement or performance levels (i.e., ordinal variables). While an order among elements can be assigned at a later stage of the analysis, at this time, it suffices to label each state with a unique element (e.g., working in finance, with high engagement, and high performance = ‘A’). Once the elements are determined and labelled, OMA requires the input dataset to be transformed into a wide format, where each row represent a case and each column a measurement occasion, so that for each case a sequence of elements arises. Missing values are common in longitudinal research but, compared to other methods, OMA handles them relatively easily. 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 dataset (3% decreased classification accuracy in Dlouhy & Biemann, 2015). Although late joiners, attrition, and other factors may cause sequences to have different