Persistence of innovation: Discriminating between types of innovation and sources of state dependence

This paper advances current research on the persistence of innovation drawing on a panel data set constructed from three waves of the German part of the Community Innovation Survey covering the time span from 2002 to 2008 (i.e., CIS IV, CIS 2007, and CIS 2009). We explore persistence patterns for various types of innovation (i.e., organizational, product and process innovation) and allowing for the moderating impact of firm-level characteristics. We use the ascertained patterns of persistence to comparatively evaluate various sources (and underlying theoretical accounts) of state dependence.     

Top-down and bottom-up approaches to improve educational quality: their intended and unintended consequences

Educational Assessment, Evaluation and Accountability -     

Robuste Indizes für Projection Pursuit

Die moderne und hoch komplexe Fertigung, z.B. in der Halbleiterindustrie, erfordert für die dabei anfallenden Messdaten der Anlagen- bzw. Prozessparameter multivariate Analysemethoden. Eine dieser m?glichen Analysemethoden ist Projection Pursuit (PP). Dieses Verfahren ist durch geschickte Wahl des so genannten Projektionsindex in der Lage, verschiedenste Datencharakteristika zu detektieren und diese auf anschauliche Weise zu visualisieren. Bei den Projektionsindizes handelt es sich um Funktionen, die eine Projektion auf unterschiedliche Merkmale hin bewerten und dabei jeder Projektion einen Funktionswert zuweisen. Dieser Funktionswert spiegelt die Aussagekraft der Projektion (in Abh?ngigkeit vom verwendeten Index) wider. Die Auswahl bzw. der Aufbau dieser Indizes ist hierbei von entscheidender Bedeutung. Alle hier vorgestellten Indizes zeichnen sich vor allem durch ihren robusten Charakter gegenüber durch Ausrei?er kontaminierte Daten, wie sie in realen Datenszenarien vorkommen, aus. Die durch Anwendung dieser Indizes gewonnenen Einblicke in die komplexe Struktur der Daten erm?glichen es, hochdimensionale Parameterabweichungen und komplexe Zusammenh?nge innerhalb der Daten zu finden.     

Vocational Education and Training: Researching the Relationship between School and Work

Researchers and policy makers agree that the relation between school and work is an important characteristic of vocational education and training (VET) and needs attention. The relation itself poses several challenges for research to be successful and useful for policy makers. In this article, we outline these challenges and answer the following question: What needs to be considered when researching the relation between school and work? Our central claim is that research on the relation between school and work needs to (1) take into account contextual conditions of the VET system, including identifying the central actors and their responsibilities within a VET system, (2) take into account the characteristics of VET itself, (3) define the quality of the relation between school and work (depending on the context, characteristics of the VET system and VET itself), and (4) be consistent with respect to chosen research aims and paradigms. To illustrate how these aspects can be taken into account an example of empirical research conducted in the German dual apprenticeship system is described. The readers are provided with guidelines for researchers and policy makers to carry out high quality research on the relation between school and work in VET systems that enables policy makers to make use of the results to improve the relation between school and work in VET systems.     

On the Performance of Likelihood-Based Difference Tests in Nonlinear Structural Equation Models

This article investigates likelihood-based difference statistics for testing nonlinear effects in structural equation modeling using the latent moderated structural equations (LMS) approach. In addition to the standard difference statistic T_D, 2 robust statistics have been developed in the literature to ensure valid results under the conditions of nonnormality or small sample sizes: the robust T_DR and the “strictly positive” T_DRP. These robust statistics have not been examined in combination with LMS yet. In 2 Monte Carlo studies we investigate the performance of these methods for testing quadratic or interaction effects subject to different sources of nonnormality, nonnormality due to the nonlinear terms, and nonnormality due to the distribution of the predictor variables. The results indicate that T_D is preferable to both T_DR and T_DRP. Under the condition of strong nonlinear effects and nonnormal predictors, T_DR often produced negative differences and T_DRP showed no desirable power.