If employees are not able to deal effectively with their emotions, negative consequences may occur such as absenteeism, turnover, and poorer physical and mental health. Therefore this study aimed...Show moreIf employees are not able to deal effectively with their emotions, negative consequences may occur such as absenteeism, turnover, and poorer physical and mental health. Therefore this study aimed to investigate if exercise fosters emotion regulation. It was hypothesized that 1) exercise would lead to less negative emotions; 2) this relationship is mediated by adaptive emotion regulation strategies, and 3) this effect would be greater for individuals high versus low in sensory processing sensitivity. To investigate this, an experimental, between-subjects design was used. A total of 134 students were recruited from Dutch universities, who were randomly assigned to one of three conditions: exercise, active control (making a puzzle), or passive control (rest). During the experiment, all participants filled out a questionnaire measuring background variables, negative state emotions, and trait sensory processing sensitivity. After this, participants underwent a negative emotion induction task to elicit negative emotions and create a need to regulate emotions. They then spent thirty minutes on a cycle ergometer, doing a puzzle, or resting in a chair, after which adaptive emotion regulation strategies and negative emotions were measured. A mixed ANCOVA was conducted to test whether participants in the exercise condition experienced less negative emotions after cycling for thirty minutes than the participants in the control conditions, which was not supported by the data. The SPSS extension ,,PROCESS” was used to test the second (model 4) and third (model 7) hypothesis. Support was found for the expectation that participants in the exercise condition make more use of adaptive emotion regulation strategies after cycling for thirty minutes, and therefore experience less negative emotions than the participants in the puzzle or rest condition (significant full mediation effect). Lastly, the hypothesis that the effect of exercise on negative emotions via emotion regulation would be greater for those scoring higher on sensory processing sensitivity than for those scoring lower on this trait, was not supported. These findings further our knowledge on the interdependence of these variables, since inconsistencies found in the literature may be explained by the indirect effect found in this study. The role of adaptive emotion regulation may be key to understanding the underlying mechanisms of how exercise influences emotions. This could potentially impact the development of creating (workplace) interventions, where combining an exercise intervention with increasing the knowledge of the employees about adaptive emotion regulation strategies might be most effective. However, more research in this area is needed to test how generalizable this effect is, since there is reason to believe gender differences and natural preferences (versus lab imposed conditions) may influence these findings.Show less
Clustering algorithms are important for data mining, and K-means is one of the most well-known clustering algorithms currently available. In cases in which data are high-dimensional, however, mere...Show moreClustering algorithms are important for data mining, and K-means is one of the most well-known clustering algorithms currently available. In cases in which data are high-dimensional, however, mere application of K-means to a data set may fail to uncover clusters due to presence of masking variables, the curse of dimensionality, and difficulties in interpretation of the obtained solution. A commonly used work-around is to apply dimension reduction to the data prior to performing cluster analysis, a practice called Tandem Analysis (TA). A vulnerability of TA is that the applied dimension reduction is not guaranteed to preserve cluster structure present in the original data, jeopardising the usefulness of subsequent cluster analysis. Multiple authors have provided algorithms that reduce dimensionality of a data set and perform cluster analysis on the reduced data, either in a sequential fashion or a simultaneous fashion, all aiming to find suitable low-dimensional representations of data while also keeping cluster structures intact. In this thesis, a novel approach to reducing dimensionality and performing cluster analysis on the low dimensional representation of the data - called SICA - is described and thoroughly tested in two systematically manipulated simulation studies and applied to three empirical data applications. Results show that SICA is a computationally efficient algorithm well able to extract components from the original data that preserve cluster structures, but that performance depends on characteristics of the data and the model of data generation. In addition, the correctness and validity of the clusterings obtained through SICA is high, although it does not always outperform currently available methods in this regard and is dependent on the same characteristics of the data and model generation as the other algorithms. Limitations and implications for future research are discussed.Show less
