Cranberry's & konijnen: over eilandcultuur

'Texelaars zijn anders', zeggen de bewoners van het Noord-Hollandse eiland om hun onderscheid met de vastelanders aan te geven. Waarin uit zich die eilandmentaliteit? In het bestaan van TESO (Texels Eigen Stoomboot Onderneming), het Ouwe Sunderklaasfeest op 12 december, het verschil tussen echte Texelse families en de import van het vasteland of in het bestaan van een Texelse Courant? Wat is precies kenmerkend voor de eilandcultuur? Welke typische rituelen hebben zij? En hoe zit het op de andere Nederlandse (voormalige) eilanden

Imputación múltiple de valores perdidos en el análisis factorial exploratorio de escalas multidimensionales: estimación de las puntuaciones de rasgos latentes

Researchers frequently have to analyze scales in which some participants have failed to respond to some items. In this paper we focus on the exploratory factor analysis of multidimensional scales (i.e., scales that consist of a number of subscales) where each subscale is made up of a number of Likert-type items, and the aim of the analysis is to estimate participants’ scores on the corresponding latent traits. Our approach uses the following steps: (1) multiple imputation creates several copies of the data, in which the missing values are imputed; (2) each copy of the data is subject to independent factor analysis, and the same number of factors is extracted from all copies; (3) all factor solutions are simultaneously orthogonally (or obliquely) rotated so that they are both (a) factorially simple, and (b) as similar to one another as possible; (4) latent trait scores are estimated for ordinal data in each copy; and (5) participants’ scores on the latent traits are estimated as the average of the estimates of the latent traits obtained in the copies. We applied the approach in a real dataset where missing responses were artificially introduced following a real pattern of non-responses and a simulation study based on artificial datasets. The results show that our approach was able to compute factor score estimates even for participants that have missing data.Los investigadores con frecuencia se enfrentan a la difícil tarea de analizar las escalas en las que algunos de los participantes no han respondido a todos los ítems. En este artículo nos centramos en el análisis factorial exploratorio de escalas multidimensionales (es decir, escalas que constan de varias de subescalas), donde cada subescala se compone de una serie de ítems de tipo Likert, y el objetivo del análisis es estimar las puntuaciones de los participantes en los rasgos latentes correspondientes. En este contexto, se propone un nuevo enfoque para hacer frente a las respuestas faltantes que se basa en (1) la imputación múltiple de las respuestas faltantes y (2) la rotación simultánea de las muestras de datos imputados. Se ha aplicado el método en una muestra de datos reales en que las respuestas que faltantes fueron introducidas artificialmente siguiendo un patrón real de respuestas faltantes, y un estudio de simulación basado en conjuntos de datos artificiales. Los resultados muestran que nuestro enfoque (en concreto, Hot-Deck de imputación múltiple seguido de rotación Consensus Promin) es capaz de calcular correctamente la puntuación factorial estimada incluso para los participantes que tienen valores perdidos

Multiple imputation in data that grow over time:A comparison of three strategies

Multiple imputation is a highly recommended technique to deal with missing data, but the application to longitudinal datasets can be done in multiple ways. When a new wave of longitudinal data arrives, we can treat the combined data of multiple waves as a new missing data problem and overwrite existing imputations with new values (re-imputation). Alternatively, we may keep the existing imputations, and impute only the new data. We may do either a full multiple imputation (nested) or a single imputation (appended) on the new data per imputed set. This study compares these three strategies by means of simulation. All techniques resulted in valid inference under a monotone missingness pattern. A non-monotone missingness pattern led to biased and non-confidence valid regression coefficients after nested and appended imputation, depending on the correlation structure of the data. Correlations within timepoints must be stronger than correlations between timepoints to obtain valid inference. In an empirical example, the three strategies performed similarly.We conclude that appended imputation is especially beneficial in longitudinal datasets that suffer from dropout

Multiple imputation in data that grow over time:A comparison of three strategies

Multiple imputation is a recommended technique to deal with missing data. We study the problem where the investigator has already created imputations before the arrival of the next wave of data. The newly arriving data contain missing values that need to be imputed. The standard method (RE-IMPUTE) is to combine the new and old data before imputation, and re-impute all missing values in the combined data. We study the properties of two methods that impute the missing data in the new part only, thus preserving the historic imputations. Method NEST multiply imputes the new data conditional on each filled-in old data (Formula presented.) times. Method APPEND is the special case of NEST with (Formula presented.) thus appending each filled-in data by single imputation. We found that NEST and APPEND have the same validity as RE-IMPUTE for monotone missing data-patterns. NEST and APPEND also work well when relations within waves are stronger than between waves and for moderate percentages of missing data. We do not recommend the use of NEST or APPEND when relations within time points are weak and when associations between time points are strong

Substrate specificity of a long-chain alkylamine-degrading Pseudomonas sp isolated from activated sludge

A bacterium strain BERT, which utilizes primary long-chain alkylamines as nitrogen, carbon and energy source, was isolated from activated sludge. This rod-shaped motile, Gram-negative strain was identified as a Pseudomonas sp. The substrate spectrum of this Pseudomonas strain BERT includes primary alkylamines with alkyl chains ranging from C3 to C18, and dodecyl-1,3-diaminopropane. Amines with alkyl chains ranging from 8 to 14 carbons were the preferred substrates. Growth on dodecanal, dodecanoic acid and acetic acid and simultaneous adaptation studies indicated that this bacterium initiates degradation through a Calkyl–N cleavage. The cleavage of alkylamines to the respective alkanals in Pseudomonas strain BERT is mediated by a PMS-dependent alkylamine dehydrogenase. This alkylamine dehydrogenase produces stoichiometric amounts of ammonium from octylamine. The PMS-dependent alkylamine was found to oxidize a broad range of long-chain alkylamines. PMS-dependent long-chain aldehyde dehydrogenase activity was also detected in cell-free extract of Pseudomonas strain BERT grown on octylamine. The proposed pathway for the oxidation of alkylamine in strain BERT proceeds from alkylamine to alkanal, and then to the fatty acid