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
Music interventions are used for stress reduction in a variety of settings because of the positive effects of music listening on both physiological arousal (e.g., heart rate, blood pressure, and hormonal levels) and psychological stress experiences (e.g., restlessness, anxiety, and nervousness). To summarize the growing body of empirical research, two multilevel meta-analyses of 104 RCTs, containing 327 effect sizes and 9,617 participants, were performed to assess the strength of the effects of music interventions on both physiological and psychological stress-related outcomes, and to test the potential moderators of the intervention effects. Results showed that music interventions had an overall significant effect on stress reduction in both physiological (d = .380) and psychological (d = .545) outcomes. Further, moderator analyses showed that the type of outcome assessment moderated the effects of music interventions on stress-related outcomes. Larger effects were found on heart rate (d = .456), compared to blood pressure (d = .343) and hormone levels (d = .349). Implications for stress-reducing music interventions are discussed.
