The aim of this study was to identify school motivation profiles of Dutch 9th grade students in a four-dimensional motivation space, including mastery, performance, social and extrinsic motivation. Multiple clustering methods (K-means, K-medoids, restricted latent profile analysis) and multiple indices for selecting the optimal number of clusters were applied. The statistical selection methods did not completely concur on the optimal number of clusters, but a clear common denominator was provided by the Calinski-Harabasz index and the minimum and mean Silhouette values. All three indices indicated two clusters as the optimal number, regardless of the clustering method used: one cluster of 9th graders with high average scores on all dimensions and one cluster with low mean scores on all dimensions. In addition, we explored the substantive interpretation of multiple cluster solutions. It was discovered that most students are in clusters that can be classified into one of three profile types that may differ in level: (1) approximately equal mean scores on all dimensions, (2) relative high mean scores on mastery and social motivation, and (3) a relatively low mean score on performance motivation. The latter profile type is believed to be a new discovery.
|Tijdschrift||Communications in Statistics Case Studies Data Analysis and Applications|
|Nummer van het tijdschrift||3|
|Status||Published - 2021|