Control Variables in Management Research

The use of control variables to improve inferences about statistical relationships in data is ubiquitous in management research. In both the micro- and macro-subfields of management, control variables are included to remove confounding variance and provide researchers with an enhanced ability to interpret findings. Scholars have explored the theoretical underpinnings and statistical effects of including control variables in a variety of statistical analyses. Further, a robust literature surrounding the best practices for their use and reporting exists. Specifically, researchers have been directed to report more detailed information in manuscripts regarding the theoretical rationale for the use of control variables, their measurement, and their inclusion in statistical analysis. Moreover, recent research indicates the value of removing control variables in many cases. Although there is evidence that articles recommending best practices for control variables use are increasingly being cited, there is also still a lag in researchers following recommendations. Finally, there are avenues for valuable future research on control variables.

Dimeric Drugs

This review intends to summarize the structures of an extensive number of symmetrical-dimeric drugs, having two monomers linked via a bridging entity while emphasizing the large versatility of biologically active substances reported to possess dimeric structures. The largest number of classes of these compounds consist of anticancer agents, antibiotics/antimicrobials, and anti-AIDS drugs. Other symmetrical-dimeric drugs include antidiabetics, antidepressants, analgesics, anti-inflammatories, drugs for the treatment of Alzheimer’s disease, anticholesterolemics, estrogenics, antioxidants, enzyme inhibitors, anti-Parkisonians, laxatives, antiallergy compounds, cannabinoids, etc. Most of the articles reviewed do not compare the activity/potency of the dimers to that of their corresponding monomers. Only in limited cases, various suggestions have been made to justify unexpected higher activity of the dimers vs. the corresponding monomers. These suggestions include statistical effects, the presence of dimeric receptors, binding of a dimer to two receptors simultaneously, and others. It is virtually impossible to predict which dimers will be preferable to their respective monomers, or which linking bridges will lead to the most active compounds. It is expected that the extensive number of articles summarized, and the large variety of substances mentioned, which display various biological activities, should be of interest to many academic and industrial medicinal chemists.

Bayesian population correlation: A probabilistic approach to inferring and comparing population distributions for detrital zircon ages

Populations of detrital zircons are shaped by geologic factors such as sediment transport, erosion mechanisms, and the zircon fertility of source areas. Zircon U-Pb age datasets are influenced both by these geologic factors and by the statistical effects of sampling. Such statistical effects introduce significant uncertainty into the inference of parent population age distributions from detrital zircon samples. This uncertainty must be accounted for in order to understand which features of sample age distributions are attributable to earth processes and which are sampling effects. Sampling effects are likely to be significant at a range of common detrital zircon sample sizes (particularly when n < 300).In order to more accurately account for the uncertainty in estimating parent population age distributions, we introduce a new method to infer probability model ensembles (PMEs) from detrital zircon samples. Each PME represents a set of the potential parent populations that are likely to have produced a given zircon age sample. PMEs form the basis of a new metric of correspondence between two detrital zircon samples, Bayesian Population Correlation (BPC), which is shown in a suite of numerical experiments to be unbiased with respect to sample size. BPC uncertainties can be directly estimated for a specific sample comparison, and BPC results conform to analytical predictions when comparing populations with known proportions of shared ages. We implement all of these features in a set of MATLAB® scripts made freely available as open-source code and as a standalone application. The robust uncertainties, lack of sample size bias, and predictability of BPC are desirable features that differentiate it from existing detrital zircon correspondence metrics. Additionally, analysis of other sample limited datasets with complex probability distributions may also benefit from our approach.

Cross-Linking Effects Dictate the Preference of Galectins to Bind LacNAc-Decorated HPMA Copolymers

The interaction of multi-LacNAc (Galβ1-4GlcNAc)-containing N-(2-hydroxypropyl) methacrylamide (HPMA) copolymers with human galectin-1 (Gal-1) and the carbohydrate recognition domain (CRD) of human galectin-3 (Gal-3) was analyzed using NMR methods in addition to cryo-electron-microscopy and dynamic light scattering (DLS) experiments. The interaction with individual LacNAc-containing components of the polymer was studied for comparison purposes. For Gal-3 CRD, the NMR data suggest a canonical interaction of the individual small-molecule bi- and trivalent ligands with the lectin binding site and better affinity for the trivalent arrangement due to statistical effects. For the glycopolymers, the interaction was stronger, although no evidence for forming a large supramolecule was obtained. In contrast, for Gal-1, the results indicate the formation of large cross-linked supramolecules in the presence of multivalent LacNAc entities for both the individual building blocks and the polymers. Interestingly, the bivalent and trivalent presentation of LacNAc in the polymer did not produce such an increase, indicating that the multivalency provided by the polymer is sufficient for triggering an efficient binding between the glycopolymer and Gal-1. This hypothesis was further demonstrated by electron microscopy and DLS methods.

Event-related components are structurally represented by intrinsic event-related potentials

The detection of event-related potentials (ERPs) through electroencephalogram (EEG) analysis is a well-established method for understanding brain functions during a cognitive process. To increase the signal-to-noise ratio (SNR) and stationarity of the data, ERPs are often filtered to a wideband frequency range, such as 0.05–30 Hz. Alternatively, a natural-filtering procedure can be performed through empirical mode decomposition (EMD), which yields intrinsic mode functions (IMFs) for each trial of the EEG data, followed by averaging over trials to generate the event-related modes. However, although the EMD-based filtering procedure has advantages such as a high SNR, suitable waveform shape, and high statistical power, one fundamental drawback of the procedure is that it requires the selection of an IMF (or a partial sum of a range of IMFs) to determine an ERP component effectively. Therefore, in this study, we propose an intrinsic ERP (iERP) method to overcome the drawbacks and retain the advantages of event-related mode analysis for investigating ERP components. The iERP method can reveal multiple ERP components at their characteristic time scales and suitably cluster statistical effects among modes by using a tailored definition of each mode’s neighbors. We validated the iERP method by using realistic EEG data sets acquired from a face perception task and visual working memory task. By using these two data sets, we demonstrated how to apply the iERP method to a cognitive task and incorporate existing cluster-based tests into iERP analysis. Moreover, iERP analysis revealed the statistical effects between (or among) experimental conditions more effectively than the conventional ERP method did.

Correlates of Aggregate Support for the Radical Right in Portugal

This research note investigates the role of contextual factors in explaining variation in local aggregate support for the populist radical right in Portugal, a country long considered immune to the rise of this political force. Using electoral results of the 2021 presidential election at the municipal level, I find significant positive statistical effects of the share of social assistance benefit recipients and the size of the Roma minority on radical right vote shares. In contrast, factors such as unemployment, average income levels or the share of immigrants do not explain radical right vote shares.

Diagnostics of interstellar medium parameters on non-discrete timing irregularities of pulsars

In this paper, a large homogenous sample of Jodrell Bank Observatory (JBO) radio pulsars was used to investigate the statistical effects of interstellar medium (ISM) parameters: dispersion and rotation measure (DM and RM, respectively) on non-discrete timing irregularities of our sample (whose observed timing activity timescales span over 40 years). This is done by using the correlations between the measured DM and RM, and some parameters that have been commonly used to measure non-discrete timing irregularities [timing activity parameter (A), the amount of timing fluctuations absorbed by the cubic term (σR23), measure of pulsar rotational stability (σz ) and stability parameter (∆8)]. Our results show that ISM parameters positively correlate (r > 0.60) with the pulsar timing irregularities parameters of our sample. The significant relationships observed are discussed.

Raincloud plots: a multi-platform tool for robust data visualization

Across scientific disciplines, there is a rapidly growing recognition of the need for more statistically robust, transparent approaches to data visualization. Complementary to this, many scientists have called for plotting tools that accurately and transparently convey key aspects of statistical effects and raw data with minimal distortion. Previously common approaches, such as plotting conditional mean or median barplots together with error-bars have been criticized for distorting effect size, hiding underlying patterns in the raw data, and obscuring the assumptions upon which the most commonly used statistical tests are based. Here we describe a data visualization approach which overcomes these issues, providing maximal statistical information while preserving the desired ‘inference at a glance’ nature of barplots and other similar visualization devices. These “raincloud plots” can visualize raw data, probability density, and key summary statistics such as median, mean, and relevant confidence intervals in an appealing and flexible format with minimal redundancy. In this tutorial paper, we provide basic demonstrations of the strength of raincloud plots and similar approaches, outline potential modifications for their optimal use, and provide open-source code for their streamlined implementation in R, Python and Matlab (https://github.com/RainCloudPlots/RainCloudPlots). Readers can investigate the R and Python tutorials interactively in the browser using Binder by Project Jupyter.