Collinearity refers to the non independence of predictor variables, usually in a regression‐
type analysis. It is a common feature of any descriptive ecological data set and can be a …

Context dependence is widely invoked to explain disparate results in ecology. It arises when
the magnitude or sign of a relationship varies due to the conditions under which it is …

Variance-based structural equation modeling is extensively used in information systems
research, and many related findings may have been distorted by hidden collinearity. This is …

Understanding how words change their meanings over time is key to models of language
and cultural evolution, but historical data on meaning is scarce, making theories hard to …

Urban areas are experiencing strongly increasing hot temperature extremes. However,
these urban heat events have seldom been the focus of traditional detection and attribution …

The adverse impact of ignoring multicollinearity on findings and data interpretation in
regression analysis is very well documented in the statistical literature. The failure to identify …

The management and conservation of the world's oceans require synthesis of spatial data
on the distribution and intensity of human activities and the overlap of their impacts on …

The use of linear mixed effects models (LMMs) is increasingly common in the analysis of
biological data. Whilst LMMs offer a flexible approach to modelling a broad range of data …

Multicollinearity is a potential problem in all regression analyses. However, the examination
of multicollinearity is rarely reported in primary studies. In this article we discuss and show …

Generalized linear mixed models (GLMMs) have been widely used in contemporary ecology
studies. However, determination of the relative importance of collinear predictors (ie fixed …