Computational typologies of multidimensional end-of-primary-school performance profiles from an educational perspective of large-scale TIMSS and PIRLS surveys

Recently, performance profiles in reading, mathematics and science were created using the data collectively available in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS) 2011. In addition, a classification of children to the end of their primary school years was conducted in accordance with these performance types. To create performance profiles and classifications, multidimensional item response theory and latent profile analysis were used. The focus in this study is on the comparison and usability of clustering methods in their application in large-scale assessments. In a first step, the cluster solutions of classic approaches such as kmeans, fuzzy c-means and hierarchical procedures are compared to one another and assessed in terms of their proximity to the reference typology of latent profile analysis. In the second step, the results of the model-supported latent profile analysis are compared directly with the findings of the classic model-free cluster analyses by means of appropriate measured values. The result is a high consistency in the classification of invariant ranked profiles. In the last step, the calculated “quantitative” cluster solutions are compared to the “qualitative” typology of the pupils derived from the content-based benchmarking of the competency levels in mathematics using the TIMSS guidelines. It is evident that, as a cluster solution, the benchmarking breakdown of the sample in the five competency levels does not show a high goodness of fit with the available data.