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Der Begriff "multilevel governance" bzw. Steuerung und Koordinierung im Mehrebenensystem wird in der politikwissenschaftlichen Literatur erst seit einiger Zeit häufiger gebraucht. Mehrebenensysteme der Politik entstehen, wenn zwar die Zuständigkeiten nach Ebenen aufgeteilt, jedoch die Aufgaben interdependent sind, wenn also Entscheidungen zwischen Ebenen koordiniert werden müssen. Die in der Föderalismusdiskussion vielfach negativ bewertete "Politikverflechtung" stellt somit ein wesentliches Merkmal von "multilevel governance" dar. Gegenstand der vorliegenden politikwissenschaftlichen Analyse sind die Ursachen, die Formen und die Folgen der Verflechtung. Ein zentrales Merkmal der Mehrebenenpolitik ergibt sich aus der Tatsache, dass mit Ebenen eine bestimmte Organisation von Politik, sei es einer Gebietskörperschaft oder einer internationalen Form der Staatenzusammenarbeit oder der Staatenverbindung, umschrieben wird. Es handelt sich damit um komplexe Konfiguration, die aus der Verbindung von Strukturen und Prozessen innerhalb von Ebenen (intragouvernemental) und zwischen Ebenen (intergouvernemental) gebildet werden. Die konkrete Form eines Mehrebenensystems resultiert aus der Kombination von institutionellen "Regelsystemen" der jeweiligen Ebenen und den Beziehungen zwischen ihnen. Diese Regelsysteme erzeugen bestimmte Funktionslogiken der Politik, die mehr oder weniger miteinander kompatibel sind. (ICA2)

Abstract:It is argued that routines can be fruitfully conceived of as multilevel mechanisms. The merits of viewing routines as multilevel mechanisms are that it helps in putting together a coherent picture of what routines are, what routines do, and how they do it. In particular, it helps in getting a clearer picture of how skills and routines are ontologically (rather than metaphorically) related to each other. It allows us to see that while routines aregenerativemechanisms producing recurrent patterns of firm behavior, asmultilevelmechanisms they themselves are at the same time recurrent patterns of interaction within firms. Because of its 'behavioral' spirit, viewing routines as multilevel mechanisms (rather than as, for example, unobservable dispositions of firms to energize patterns of behavior in firms) greatly facilitates further empirical research on crucial, as yet unresolved issues, such as how stable and robust routines are and to what extent firm behavior is routine.

Multilevel data are structures that consist of multiple units of analysis, one nested within the other. Such data are becoming quite common in political science & provide numerous opportunities for theory testing & development. Unfortunately, this type of data typically generates a number of statistical problems, of which clustering is particularly important. To exploit the opportunities offered by multilevel data, & to solve the statistical problems inherent in them, special statistical techniques are required. In this article, we focus on a technique that has become popular in educational statistics & sociology -- multilevel analysis. In multilevel analysis, researchers build models that capture the layered structure of multilevel data, & determine how layers interact & impact a dependent variable of interest. Our objective in this article is to introduce the logic & statistical theory behind multilevel models, to illustrate how such models can be applied in political science, & to call attention to some of the pitfalls in multilevel analysis. 5 Tables, 96 References. Adapted from the source document.

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Authors -- 1: Linear Models -- Simple Linear Regression -- Estimating Regression Models with Ordinary Least Squares -- Distributional Assumptions Underlying Regression -- Coefficient of Determination -- Inference for Regression Parameters -- Multiple Regression -- Example of Simple Linear Regression by Hand -- Regression in R -- Interaction Terms in Regression -- Categorical Independent Variables -- Checking Regression Assumptions with R -- Summary -- 2: An Introduction to Multilevel Data Structure -- Nested Data and Cluster Sampling Designs -- Intraclass Correlation -- Pitfalls of Ignoring Multilevel Data Structure -- Multilevel Linear Models -- Random Intercept -- Random Slopes -- Centering -- Basics of Parameter Estimation with MLMs -- Maximum Likelihood Estimation -- Restricted Maximum Likelihood Estimation -- Assumptions Underlying MLMs -- Overview of Two-Level MLMs -- Overview of Three-Level MLMs -- Overview of Longitudinal Designs and Their Relationship to MLMs -- Summary -- 3: Fitting Two-Level Models in R -- Simple (Intercept-Only) Multilevel Models -- Interactions and Cross-Level Interactions Using R -- Random Coefficients Models using R -- Centering Predictors -- Additional Options -- Parameter Estimation Method -- Estimation Controls -- Comparing Model Fit -- lme4 and Hypothesis Testing -- Summary -- Note -- 4: Three-Level and Higher Models -- Defining Simple Three-Level Models Using the lme4 Package -- Defining Simple Models with More than Three Levels in the lme4 Package -- Random Coefficients Models with Three or More Levels in the lme4 Package -- Summary -- Note -- 5: Longitudinal Data Analysis Using Multilevel Models -- The Multilevel Longitudinal Framework -- Person Period Data Structure -- Fitting Longitudinal Models Using the lme4 Package.

AbstractThe distinction between multilevel selection 1 (MLS1) and multilevel selection 2 (MLS2) is classically regarded as a distinction between two multilevel selection processes involving two different kinds of higher-level fitness. It has been invoked to explain evolutionary transitions in individuality as a shift from an MLS1 to an MLS2 process. In this paper, I argue against the view that the distinction involves two different kinds of processes. I show, starting from the MLS2 version of the Price equation, that it contains the MLS1 version if, following the assumption that a collective constitutively depends (i.e., mereologically supervenes) on its particles, one considers that a necessary map between fitness at two levels exists. I defend the necessity of such a map, making the distinction between MLS1 and MLS2 a matter of perspective and limited knowledge (i.e., epistemic limitations) rather than objective facts. I then provide some reasons why the MLS1/MLS2 distinction nonetheless has some pragmatic value and might be invoked usefully in some contexts, particularly within the context of explaining evolutionary transitions in individuality.