Ridderprint

Chapter 3 56 is either a theoretical justification for the assumption that the costs are the same independent of the direction of the movement […] or if one of the directions is impossible ” (p.430). In HRM research, phenomena like positive and negative spirals (Fredrickson & Joiner, 2002) may, for instance, cause transitions towards the extremes of the engagement continuum to be more frequent than the other way around, invalidating this assumption of directional independence. Second, data-based substitution costs will reflect the within- sequence variability of elements and, therefore, the chosen timespan of elements has a strong influence. This becomes evident once applied to our engagement example. Assuming engagement fluctuates over time, element transitions would occur relatively frequent when longer-spaced timespans, like yearly measurements, are used. In contrast, element repetition would occur frequently in case of hourly observations. Both timespans have consequences for the data-based substitution costs and, while neither necessarily deteriorates results, researchers should consider that an interdependency exists. 3.3.3 Clustering the Sequences Once the penalty costs are determined, the optimal matching algorithm can assess the (dis)similarity of each dyad of sequences by aligning them. Although there may be several ways to align two sequences, the algorithm seeks the one with the least penalty costs. As illustrated earlier, assigning custom substitution costs would thus make the algorithm more prone to use substitution. The process of sequence alignment is repeated for all dyads in the dataset and the resulting penalty costs are stored in a Euclidian distance matrix, named the dissimilarity matrix. Next, a classification algorithm can be applied to the dissimilarity matrix. Any unsupervised learning algorithm that handles Euclidian distance matrices can be used. However, Dlouhy and Biemann (2015) “ do not recommend using k-means, median, centroid and single linkage clustering for OMA at all ” (p.171). Out of the eight techniques they tested, Ward’s minimum variance method consistently performed best. Irrespective of the chosen algorithm, the result is a categorical variable where cases are assigned to a category based on the underlying patterns in their sequence. In the engagement example, employees will be grouped based on the patterns that have occurred in their weekly engagement levels. One can expect to find, for example, clusters of consistently disengaged, neutral, and engaged employees. Similarly, other clusters may include employees whose engagement has been steadily rising or falling, whose engagement demonstrates certain cyclical patterns, or whose engagement fluctuates randomly. Depending on the assigned penalty costs and the number of clusters, employees with missing values will be added either to the regular clusters or to clusters with specific, recurring patterns of missing values. OMA’s output can be valuable to researcher and practice in at least four ways. First, OMA’s relatively simple implementation and interpretation makes it an effective tool to get descriptive insights in longitudinal data patterns. Second, OMA can be useful for identification purposes. For example, based on the cluster output, researchers can effectively identify which employees display certain (dis)engagement patterns and reach out with follow-up interviews or (supportive) interventions. Third, the output can be used as an independent variable in subsequent analyses to study the consequences of following