Traditional Measures Of Variability Research Articles

Dynamics of Remote Communication: Movement Coordination in Video-Mediated and Face-to-Face Conversations

The present pandemic forced our daily interactions to move into the virtual world. People had to adapt to new communication media that afford different ways of interaction. Remote communication decreases the availability and salience of some cues but also may enable and highlight others. Importantly, basic movement dynamics, which are crucial for any interaction as they are responsible for the informational and affective coupling, are affected. It is therefore essential to discover exactly how these dynamics change. In this exploratory study of six interacting dyads we use traditional variability measures and cross recurrence quantification analysis to compare the movement coordination dynamics in quasi-natural dialogues in four situations: (1) remote video-mediated conversations with a self-view mirror image present, (2) remote video-mediated conversations without a self-view, (3) face-to-face conversations with a self-view, and (4) face-to-face conversations without a self-view. We discovered that in remote interactions movements pertaining to communicative gestures were exaggerated, while the stability of interpersonal coordination was greatly decreased. The presence of the self-view image made the gestures less exaggerated, but did not affect the coordination. The dynamical analyses are helpful in understanding the interaction processes and may be useful in explaining phenomena connected with video-mediated communication, such as “Zoom fatigue”.

Anchored in 'average thinking' in studies of arid rangeland dynamics – The need for a step forward from traditional measures of variability

There is a wide consensus that arid and semi-arid environments are highly variable in space and time. The most frequent approaches to describe and analyze arid and semi-arid rangelands are based on the usage of averages and Normal distributions as main references to describe spatial and temporal differences. We argue that rangeland ecology science should move forward in methods to better capture the patterns of rangeland dynamics, rather than just focusing on simple measures of variability. We provide different simulated time series data in order to illustrate the limitations of some currently used methodologies. We call for the application of time series methods in order to better understand complex dynamics in arid and semi-arid rangelands.

Blood Pressure Variability

A series of articles1–4⇓⇓⇓ published recently in The Lancet and Lancet Neurology raise an interesting issue that has implications for both the clinical management of hypertension and future research in hypertension, particularly in the development and use of different classes of blood pressure (BP)–lowering drugs. These studies, which were led by Peter Rothwell at the John Radcliffe Hospital in Oxford, United Kingdom, suggest that, whereas there is undoubted and well-proven benefit in the current practice of reducing mean BP to prevent cardiovascular events, there may be additional benefit in also reducing BP variability (BPV), especially to prevent stroke. The studies suggest, moreover, that different classes of drugs are superior to others in reducing BPV (calcium channel blockers being best and the β-blocker atenolol being worst). However, these articles, by virtue of their sheer volume (≈50 pages of printed text and many pages of supplementary web appendix data), could overwhelm all but the most stoic readers, and misinterpretation of the data could lead to confusion and have an adverse effect on clinical practice. It is important, therefore, to assess the scientific reality and determine how attention to BPV might benefit patients with hypertension. In the first analysis, systolic BPV between visits and maximum BP reached in 4 cohorts of patients with previous transient ischemic attacks were strong predictors for subsequent stroke.1 In treated hypertensive patients in the Anglo-Scandinavian Cardiac Outcome Trial-Blood Pressure Lowering Arm systolic BPV between visits was also a strong predictor of stroke and coronary events independent of mean clinic or ambulatory BP measurement (ABPM). BPV on ABPM was a weaker predictor overall but was related to visit-to-visit variability. Traditional measures of variability, such as SD and coefficient of variation (CV), were used in these analyses, but one of the problems encountered in the …

Monitoring process parameters with pre-control

Abstract Many manufacturers have reported success in using Pre-Control (PC) procedures to monitor their processes. The reported success has been attributed to the fact that the operators, who usually implement PC procedure, find it simple. Beside simplicity, PC has several other advantages over conventional control charts ( X , R, X charts, etc.). PC ensures operator control of a process as well as product quality, an essential factor in operator empowerment. The procedure can also be effectively used on short production runs; it ensures quick process qualification; provides quick feedback of information on a process; and is very economical to operate because it requires minimum paperwork and mathematical calculations. The simplicity of use is attributed to the fact that, in its easiest and most common mode of implementation, PC requires no numerical recording or scaled plotting of data. For these same reasons it has been difficult, in this mode of implementation, to evaluate the values of the process parameters (mean and variability). In the other mode of PC implementation where numerical recording and plotting of data is routinely required, the simplicity advantage is lost. Because of the inability to evaluate the process parameters, manufacturers using the common mode of PC implementation are frustrated because they are unable to demonstrate that their processes are capable of, and are in fact producing, products of high quality. In this paper we propose two models for evaluating process parameters without the need to record any numerical measurement of product characteristics. Although the two models are jointly executed, the first evaluates process variability, and the second process mean. Common PC procedure normally uses indicator gages with zones colored identically as the zones of the ‘charts’. We show that the only additional requirement to execute the models is to subdivide each of the four zones on the gage into two. The ‘chart’ itself needs not be drawn to scale. The operator can draw the required lines on the ‘chart’ free-hand. Points representing zones indicated by the gage's “measurements” are marked anywhere in a “box” on the chart without the need for accurately locating the points. We conducted a simulation analysis to evaluate the accuracy of our models (measured by the absolute percent error of the estimates of the process parameters from the actual). We report that our models perform favorably compared with traditional measures of variability and mean. For manufacturers who use the simple PC procedures we believe that the proposed models now provide a means to also quantify the process capability.