Sebastian Weirich

Zusammenfassung: Um die Unterrichtsversorgung zu sichern, setzen viele Grundschulen in Deutschland Lehrkräfte fach- und schulartfremd oder ohne grundständige Lehramtsausbildung im Unterricht ein. Der vorliegende Beitrag untersucht die Auswirkungen dieses Einsatzes auf die Unterrichtsqualität und die Leistungen der Schüler:innen in der Jahrgangsstufe 4. Auf Basis der Daten des IQB-Bildungstrends 2021 erfolgte basierend auf Daten von N = 26.844 Viertklässler:innen und N = 1738 Lehrkräften zunächst eine Aufbereitung mittels Multilevel Propensity Score Matching. Anschließend wurden Multilevel-Regressionsanalysen durchgeführt, um die Effekte unterschiedlicher Einsätze von Lehrkräften jeweils im Vergleich zu regulär eingesetzten Lehrkräften unter Kontrolle von Merkmalen der Schüler:innen, der Klassenzusammensetzung und der Lehrkraft zu analysieren. Die Ergebnisse zeigen, dass sich die wahrgenommene Unterrichtsqualität durch die Schüler:innen in den Fächern Deutsch und Mathematik in Klassen mit fachfremden, schulartfremden oder nicht grundständig ausgebildeten Lehrkräften gegenüber regulär eingesetzten Lehrkräften nicht unterscheidet. Hinsichtlich der Leistungen der Schüler:innen zeigen sich nur im Fach Deutsch und nur für die von schulartfremden Lehrkräften unterrichteten Schüler:innen etwas geringere Kompetenzen. Der Beitrag schließt mit einer Diskussion der Implikationen für die Bildungsadministration und einem Ausblick auf weitere Forschungsbedarfe.

The article compares the trajectories of students’ self-reported test-taking effort during a 120 minutes low-stakes large-scale assessment of English comprehension between a paper-and-pencil (PPA) and a computer-based assessment (CBA). Test-taking effort was measured four times during the test. Using a within-subject design, each of the N = 2,676 German ninth-grade students completed half of the test in PPA and half in CBA mode, where the sequence of modes was balanced between students. Overall, students’ test-taking effort decreased considerably during the course of the test. On average, effort was lower in CBA than in PPA. While on average, effort was lower in CBA than in PPA, the decline did not vary between both modes during the test. That is, students’ self-reported effort was higher if the items were easier (compared to students’ abilities). The consequences of these results concerning the further development of CBA tests and large-scale assessments in general are discussed.

In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster.

Bayesian MCMC is a widely used model estimation technique, and software from the BUGS family, such as JAGS, have been popular for over two decades. Recently, Stan entered the market with promises of higher efficiency fueled by advanced and more sophisticated algorithms. With this study, we want to contribute empirical results to the discussion about the sampling efficiency of JAGS and Stan. We conducted three simulation studies in which we varied the number of warmup iterations, the prior informativeness, and sample sizes and employed the multi-level intercept-only model in the covariance- and mean-based and in the classic parametrization. The target outcome was MCMC efficiency measured as effective sample size per second (ESS/s). Based on our specific (and limited) study setup, we found that (1) MCMC efficiency is much higher for the covariance- and mean-based parametrization than for the classic parametrization, (2) Stan clearly outperforms JAGS when the covariance- and mean-based parametrization is used, and that (3) JAGS clearly outperforms Stan when the classic parametrization is used.

In many disciplines of the social sciences, comparisons between a group mean and the total mean is a common but also challenging task. As one solution to t