BACKGROUND Outcomes research often compares patient and organizational outcomes across institutions, dealing with variables measured at different hierarchical levels. A traditional approach to analyzing multilevel data has been to aggregate individual-level variables at the institutional level. OBJECTIVES To introduce the conceptual and statistical background of multilevel analysis and provid...

This paper presents equivalent models of Modular Marx Multilevel Converter Diode (MCD) plus load, for bipolar or unipolar operation. With these models, it’s possible to analyze and understand the dynamic behavior of the pulse output voltage and current at the load. Simulation and experimental results, for different voltage levels, and various types of load are presented. Keywords-Modeling , Mul...

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to...

In the past 2 decades latent variable modeling has become a standard tool in the social sciences. In the same time period, traditional linear structural equation models have been extended to include non-linear interaction and quadratic effects (e.g., Klein and Moosbrugger, 2000), and multilevel modeling (Rabe-Hesketh et al., 2004). We present a general non-linear multilevel structural equation ...

Many classroom climate studies suffer from two critical problems. They: (a) treat climate as a studentlevel (L1) variable in single-level analyses instead of a classroom-level (L2) construct in multilevel analyses; and (b) rely on manifest-variable models rather than latent-variable models that control measurement error at L1 and L2, and sampling error in the aggregation of L1 ratings to form L...

Multilevel models have become popular for the analysis of a variety of problems, going beyond the classical individuals-within-groups applications. This chapter gives a brief summary of the reasons for using multilevel models, and provides examples why these reasons are indeed valid reasons. Next, recent (simulation) research is reviewed on the robustness and power of the usual estimation proce...

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data gen...

In this paper a two-level regression model is imposed on the ability parameters in an IRT model. The advantage of using latent rather than observed scores as dependent variables of a multi-level model is that this offers the possibility of separating the influence of item difficulty and ability level and modeling response variation and measurement error. Another advantage is that, contrary to o...