Variability indices are a key measure of interest across diverse fields, in and outside psychology. A crucial problem for any research relying on variability measures however is that variability is severely confounded with the mean, especially when measurements are bounded, which is often the case in psychology (e.g., participants are asked "rate how happy you feel now between 0 and 100?"). While a number of solutions to this problem have been proposed, none of these are sufficient or generic. As a result, conclusions on the basis of research relying on variability measures may be unjustified. Here, we introduce a generic solution to this problem by proposing a relative variability index that is not confounded with the mean by taking into account the maximum possible variance given an observed mean. The proposed index is studied theoretically and we offer an analytical solution for the proposed index. Associated software tools (in R and MATLAB) have been developed to compute the relative index for measures of standard deviation, relative range, relative interquartile distance and relative root mean squared successive difference. In five data examples, we show how the relative variability index solves the problem of confound with the mean, and document how the use of the relative variability measure can lead to different conclusions, compared with when conventional variability measures are used. Among others, we show that the variability of negative emotions, a core feature of patients with borderline disorder, may be an effect solely driven by the mean of these negative emotions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out some limitations of the IV estimator when the instrumental variable is only weakly correlated with the exposure. The IV estimator will be imprecise (large standard error), biased when sample size is small, and biased in large samples when one of the assumptions is only slightly violated. For these reasons, it is advised to use an IV that is strongly correlated with exposure. However, we further show that under the assumptions required for the validity of the method, this correlation between IV and exposure is limited. Its maximum is low when confounding is strong, such as in case of confounding by indication. Finally, we show that in a study in which strong confounding is to be expected and an IV has been used that is moderately or strongly related to exposure, it is likely that the assumptions of IV are violated, resulting in a biased effect estimate. We conclude that instrumental variables can be useful in case of moderate confounding but are less useful when strong confounding exists, because strong instruments cannot be found and assumptions will be easily violated.

Studying the temporal dynamics of bistable perception can be useful for understanding neural mechanisms underlying the phenomenon. We take a closer look at those temporal dynamics, using data from four different ambiguous stimuli. We focus our analyses on two recurrent themes in bistable perception literature. First, we address the question whether percept durations follow a gamma distribution, as is commonly assumed. We conclude that this assumption is not justified by the gamma distribution's approximate resemblance to distributions of percept durations. We instead present two straightforward distributions of reciprocal percept durations (i.e., rates) that both easily surpass the classic gamma distribution in terms of resemblance to empirical data. Second, we compare the distributions arising from binocular rivalry with those from other forms of bistable perception. Parallels in temporal dynamics between those classes of stimuli are often mentioned as an indication of a similar neural basis, but have never been studied in detail. Our results demonstrate that the distributions arising from binocular rivalry and other forms of bistable perception are indeed similar up to a high level of detail.

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Propensity score methods give in general treatment effect estimates that are closer to the true marginal treatment effect than a logistic regression model in which all confounders are modelled.

We conclude that measures for balance are useful for reporting the amount of balance reached in propensity score analysis and can be helpful in selecting the final PS model.

SummaryMycelial fungi play a central role in element cycling in nature by degrading dead organic material such as wood. Fungal colonization of a substrate starts with the invasion of exploring hyphae. These hyphae secrete enzymes that convert the organic material into small molecules that can be taken up by the fungus to serve as nutrients. Using green fluorescent protein (GFP) as a reporter, we show for the first time that exploring hyphae of Aspergillus niger differentiate with respect to enzyme secretion; some strongly express the glucoamylase gene glaA , while others hardly express it at all. When a cytoplasmic GFP was used, 27% of the exploring hyphae of a 5-day-old colony belonged to the low expressing hyphae. By fusing GFP to glucoamylase and by introducing an ER retention signal, this number increased to 50%. This difference is due to cytoplasmic streaming of the reporter in the former case, as was shown by using a photo-activatable GFP. Our findings indicate that a fungal mycelium is highly differentiated, especially when taking into account that hyphae in the exploration zone were exposed to the same nutritional conditions.