The article engages with the recent studies on multilevel regulation. The starting point for the argument is that contemporary multilevel regulation—as most other studies of (postnational) rulemaking—is limited in its analysis. The limitation concerns its monocentric approach that, in turn, deepens the social illegitimacy of contemporary multilevel regulation. The monocentric approach means that the study of multilevel regulation originates in the discussions on the foundation of modern States instead of returning to the origins of rules before the nation State was even created, which is where the actual social capital underlying (contemporary) rules can be found, or so I wish to argue. My aim in this article is to reframe the debate. I argue that we have an enormous reservoir of history, practices, and ideas ready to help us think through contemporary (social) legitimacy problems in multilevel regulation: namely all those practices which preceded the capture of law by the modern State system, such as historical alternative dispute resolution (ADR) practices.
ObjectiveTo compare estimates of effect and variability resulting from standard linear regression analysis and hierarchical multilevel analysis with cross-classified multilevel analysis under various scenarios.Study design and settingWe performed a simulation study based on a data structure from an observational study in clinical mental health care. We used a Markov chain Monte Carlo approach to simulate 18 scenarios, varying sample sizes, cluster sizes, effect sizes and between group variances. For each scenario, we performed standard linear regression, multilevel regression with random intercept on patient level, multilevel regression with random intercept on nursing team level and cross-classified multilevel analysis.ResultsApplying cross-classified multilevel analyses had negligible influence on the effect estimates. However, ignoring cross-classification led to underestimation of the standard errors of the covariates at the two cross-classified levels and to invalidly narrow confidence intervals. This may lead to incorrect statistical inference. Varying sample size, cluster size, effect size and variance had no meaningful influence on these findings.ConclusionIn case of cross-classified data structures, the use of a cross-classified multilevel model helps estimating valid precision of effects, and thereby, support correct inferences.
MULTIFILE
Symposiumbijdrage conferentie EARLI SIG 14, 11-14 september 2018, Genève Learning across the contexts of school and the workplace is highly relevant to the VET-sector. This contribution analyses these cross-contextual learning processes with three key issues in mind: (1) guidance by vocational educators, (2) assessment of students’ development and (3) design of VET-learning environments. Guidance, assessment and overarching VET-curriculum designs form the basis for constructive alignment as an approach to optimize conditions for high quality cross-contextual learning processes. We used the theoretical framework of boundary crossing to clarify the complex, multilevel nature of these key issues.
