Modelling method effects as individual causal effects

Summary.  Method effects often occur when different methods are used for measuring the same construct. We present a new approach for modelling this kind of phenomenon, consisting of a definition of method effects and a first model, the method effect model , that can be used for data analysis. This model may be applied to multitrait–multimethod data or to longitudinal data where the same construct is measured with at least two methods at all occasions. In this new approach, the definition of the method effects is based on the theory of individual causal effects by Neyman and Rubin. Method effects are accordingly conceptualized as the individual effects of applying measurement method j instead of k . They are modelled as latent difference scores in structural equation models. A reference method needs to be chosen against which all other methods are compared. The model fit is invariant to the choice of the reference method. The model allows the estimation of the average of the individual method effects, their variance, their correlation with the traits (and other latent variables) and the correlation of different method effects among each other. Furthermore, since the definition of the method effects is in line with the theory of causality, the method effects may (under certain conditions) be interpreted as causal effects of the method. The method effect model is compared with traditional multitrait–multimethod models. An example illustrates the application of the model to longitudinal data analysing the effect of negatively (such as 'feel bad') as compared with positively formulated items (such as 'feel good') measuring mood states.     

Negative situation appraisal and mental well-being among refugees in Germany: Serial mediation by religious coping and sense of coherence

It has been widely demonstrated that pre- and post-migration experiences can be highly traumatic for refugees. The purpose of this study was to assess the mediating effects of religious coping and sense of coherence on the association of negative dysfunctional situation appraisal and mental well-being among refugees. Paper surveys were completed by 600 people representing various refugee groups (mostly from Mashreq Countries) in German Bavaria, aged 18–65. Mediation analysis revealed that negative religious coping and sense of coherence singly and serially mediated the relationship between negative dysfunctional situation appraisal and well-being, whereas positive religious coping did not have statistically significant relationships with these variables. The data obtained suggest that the negative religious coping style and sense of coherence are two important targets for future experimental studies, and interventions on them may bring the synergistic effect on improving mental well-being in refugees.     

„Unkündbar und nun?“ Lebensziel oder Schicksalsschlag – Chancen für eine faire Trennungskultur in der öffentlichen Verwaltung

Job security is seen as a very high value by employees regarding their professional objectives. Especially for employees in public service that secure professional status is one of their main reasons for working in public servance. Therefore, any dismissal may have severe negative effects on health of the employees and also negative effects on the image of the employer. From their perspective, an outplacement program could be an expedient element within the employee-life-cycle (recruiting—retention—exit/outplacement). A fair culture of separation in the organizational context in public sector which is accompanied by an outplacement program can give new perspectives to the employees and strengthen the image of the employer. An interview study with clients of an outplacement consultation will be mentioned. The participants were asked about their expectations and experiences with the consultation. Basically, the results clearly show that the consultant has a tremendous importance and is thus one of the most important success factors. If he or she meets the needs of the client, he or she works up the separation and promotes the confidence of his clients, nothing stands in the way of a successful consultation.     

The Signed Mantel test to cope with autocorrelation in comparative analyses

In biology, medicine and anthropology, scientists try to reveal general patterns when comparing different sampling units such as biological taxa, diseases or cultures. A problem of such comparative data is that standard statistical procedures are often inappropriate due to possible autocorrelation within the data. Widespread causes of autocorrelation are a shared geography or phylogeny of the sampling units. To cope with possible autocorrelations within comparative data, we suggest a new kind of the Mantel test. The Signed Mantel test evaluates the relationship between two or more distance matrices and allows trait variables facultatively to be represented as signed distances (calculated as signed differences or quotients). Considering the sign of distances takes into account the direction of an effect found in the data. Since different metrics exist to calculate the distance between two sampling units from the raw data and because the test results often depend on the kind of metric used, we suggest validating analysis by comparing the structures of the raw and the distance data. We offer a computer program that is able to construct both signed and absolute distance matrices, to perform both customary and Signed Mantel tests, and to explore raw and distance data visually.     

A parsimonious multivariate poisson model for market basket analysis

Intensified research on multivariate Poisson models offers new opportunities for the analysis of purchase quantities in market basket data. The investigation of positive or negative correlations in quantity decisions among product categories facilitates a deeper understanding of consumer purchase behavior. The applied multivariate log-normal Poisson model introduces interdependencies between categories with multivariate normal-distributed latent effects by means of a covariance matrix. As the size of this covariance matrix depends on the number of categories in the model, its ation may become tedious. Furthermore, we assume that quantity decisions do not interact for all pairs of categories. That is why we propose to use covariance selection to derive a parsimonious representation of the correlation structure. For two market basket data sets, we show that the vast majority of off-diagonal elements in the covariance matrix are irrelevant. For a data set with product categories, the model with a partly restricted covariance matrix achieves a better fit to the holdout data than the model with full covariance matrix. For a data set with subcategories of the broader category beverage, the proposed model with restricted covariance outperforms the model with full covariance matrix even on the calibration data. We conclude that interactions of quantity decisions are overall the exception, even for complements-in-use.     

A Look at State‐Level Risk Assessment in the United States: Making Decisions in the Absence of Federal Risk Values

State environmental agencies in the United States are charged with making risk management decisions that protect public health and the environment while managing limited technical, financial, and human resources. Meanwhile, the federal risk assessment community that provides risk assessment guidance to state agencies is challenged by the rapid growth of the global chemical inventory. When chemical toxicity profiles are unavailable on the U.S. Environmental Protection Agency's Integrated Risk Information System or other federal resources, each state agency must act independently to identify and select appropriate chemical risk values for application in human health risk assessment. This practice can lead to broad interstate variation in the toxicity values selected for any one chemical. Within this context, this article describes the decision‐making process and resources used by the federal government and individual U.S. states. The risk management of trichloroethylene (TCE) in the United States is presented as a case study to demonstrate the need for a collaborative approach among U.S. states toward identification and selection of chemical risk values while awaiting federal risk values to be set. The regulatory experience with TCE is contrasted with collaborative risk science models, such as the European Union's efforts in risk assessment harmonization. Finally, we introduce State Environmental Agency Risk Collaboration for Harmonization, a free online interactive tool designed to help to create a collaborative network among state agencies to provide a vehicle for efficiently sharing information and resources, and for the advancement of harmonization in risk values used among U.S. states when federal guidance is unavailable.