Is biotechnology (more) acceptable when it enables a reduction in phytosanitary treatments? A European comparison of the acceptability of transgenesis and cisgenesis

Reduced pesticide use is one of the reasons given by Europeans for accepting new genetic engineering techniques. According to the advocates of these techniques, consumers are likely to embrace the application of cisgenesis to apple trees. In order to verify the acceptability of these techniques, we estimate a Bayesian multilevel structural equation model, which takes into account the multidimensional nature of acceptability and individual, national, and European effects, using data from the Eurobarometer 2010 73.1 on science. The results underline the persistence of clear differences between European countries and whilst showing considerable defiance, a relatively wider acceptability of vertical gene transfer as a means of reducing phytosanitary treatments, compared to horizontal transfer

Fixed and random effects models: making an informed choice

This paper assesses the options available to researchers analysing multilevel (including longitudinal) data, with the aim of supporting good methodological decision-making. Given the confusion in the literature about the key properties of fixed and random effects (FE and RE) models, we present these models’ capabilities and limitations. We also discuss the within-between RE model, sometimes misleadingly labelled a ‘hybrid’ model, showing that it is the most general of the three, with all the strengths of the other two. As such, and because it allows for important extensions—notably random slopes—we argue it should be used (as a starting point at least) in all multilevel analyses. We develop the argument through simulations, evaluating how these models cope with some likely mis-specifications. These simulations reveal that (1) failing to include random slopes can generate anti-conservative standard errors, and (2) assuming random intercepts are Normally distributed, when they are not, introduces only modest biases. These results strengthen the case for the use of, and need for, these models

Kontrolle der longitudinalen Moden in Halbleiterinjektionslasern

With 89 refs.SIGLECopy held by FIZ Karlsruhe; available from UB/TIB Hannover / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Laser fuer Teilnehmeranschluss

With 63 refs., 49 figs.SIGLETIB: FR 1439+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Using a multilevel structural equation modeling approach to explain cross-cultural measurement noninvariance

Testing for invariance of measurements across groups (such as countries or time points) is essential before meaningful comparisons may be conducted. However, when tested, invariance is often absent. As a result, comparisons across groups are potentially problematic and may be biased. In the current study, we propose utilizing a multilevel structural equation modeling (SEM) approach to provide a framework to explain item bias. We show how variation in a contextual variable may explain noninvariance. For the illustration of the method, we use data from the second round of the European Social Survey (ESS)

A review of Multilevel Modeling: some methodological issues and advances

Multilevel modeling is a recently new class of statistical methods to handle nested data. Mainly thanks to the wide range of applicability and the great increase of statistical softwares, in the last decades multilevel modeling has enjoyed an explosion of published papers and books in both methodological and application field. Currently, there is a need to not only develop the research on multilevel approach for the analysis of complex data, but also to have instructions to properly address the usage. This work aims at summarizing methodological aspects related to multilevel models, illustrating good-practices, advantages and limits by reviewing applications in various fields, such as socio-economic, educational, health and medical sciences.We further focus our attention on the latest advances of multilevel modeling towards, e.g., the inclusion of latent variables and the Bayesian approach