Written probability gambles are more certain than graphical displays

2000 ◽  
Author(s):  
Dean Yoshizumi ◽  
Irwin P. Levin
Keyword(s):  
Graphical Displays

Graphical displays of the motion of spin systems

1996 ◽  
Vol 89 (5) ◽  
pp. 1397-1408 ◽  
Author(s):  
LAWRENCE LOHR
Keyword(s):  
Spin Systems ◽  
Graphical Displays

Power-Enhanced Funnel Plots for Meta-Analysis

2020 ◽  
Vol 228 (1) ◽  
pp. 43-49 ◽  
Author(s):  
Michael Kossmeier ◽  
Ulrich S. Tran ◽  
Martin Voracek
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Relevant Information ◽  
Selective Reporting ◽  
Graphical Display ◽  
Funnel Plot ◽  
Evidential Value ◽  
Graphical Displays ◽  
Funnel Plots ◽  
Meta Analyses

Abstract. Currently, dedicated graphical displays to depict study-level statistical power in the context of meta-analysis are unavailable. Here, we introduce the sunset (power-enhanced) funnel plot to visualize this relevant information for assessing the credibility, or evidential value, of a set of studies. The sunset funnel plot highlights the statistical power of primary studies to detect an underlying true effect of interest in the well-known funnel display with color-coded power regions and a second power axis. This graphical display allows meta-analysts to incorporate power considerations into classic funnel plot assessments of small-study effects. Nominally significant, but low-powered, studies might be seen as less credible and as more likely being affected by selective reporting. We exemplify the application of the sunset funnel plot with two published meta-analyses from medicine and psychology. Software to create this variation of the funnel plot is provided via a tailored R function. In conclusion, the sunset (power-enhanced) funnel plot is a novel and useful graphical display to critically examine and to present study-level power in the context of meta-analysis.

Which Data to Meta-Analyze, and How?

2019 ◽  
Vol 227 (1) ◽  
pp. 64-82 ◽  
Author(s):  
Martin Voracek ◽  
Michael Kossmeier ◽  
Ulrich S. Tran
Keyword(s):  
Empirical Research ◽  
Meta Analysis ◽  
Curve Analysis ◽  
Research Progress ◽  
Primary Data ◽  
Statistical Procedures ◽  
Graphical Displays ◽  
Primary Data Analysis ◽  
Meta Analyses ◽  
Data Analytic

Abstract. Which data to analyze, and how, are fundamental questions of all empirical research. As there are always numerous flexibilities in data-analytic decisions (a “garden of forking paths”), this poses perennial problems to all empirical research. Specification-curve analysis and multiverse analysis have recently been proposed as solutions to these issues. Building on the structural analogies between primary data analysis and meta-analysis, we transform and adapt these approaches to the meta-analytic level, in tandem with combinatorial meta-analysis. We explain the rationale of this idea, suggest descriptive and inferential statistical procedures, as well as graphical displays, provide code for meta-analytic practitioners to generate and use these, and present a fully worked real example from digit ratio (2D:4D) research, totaling 1,592 meta-analytic specifications. Specification-curve and multiverse meta-analysis holds promise to resolve conflicting meta-analyses, contested evidence, controversial empirical literatures, and polarized research, and to mitigate the associated detrimental effects of these phenomena on research progress.

scholarly journals Communicating evidence in icons and summary formats for policymakers: what works?

2021 ◽  
pp. 1-29
Author(s):  
Cameron Brick ◽  
Alexandra L.J. Freeman
Keyword(s):  
Decision Makers ◽  
Policy Options ◽  
Negative Consequences ◽  
Graphical Displays ◽  
What Works ◽  
Financial Costs ◽  
Two Samples ◽  
Evidence Quality ◽  
Color Cues ◽  
Expected Outcomes

Abstract Policy decisions have vast consequences, but there is little empirical research on how best to communicate underlying evidence to decision-makers. Groups in diverse fields (e.g., education, medicine, crime) use brief, graphical displays to list policy options, expected outcomes and evidence quality in order to make such evidence easy to assess. However, the understanding of these representations is rarely studied. We surveyed experts and non-experts on what information they wanted and tested their objective comprehension of commonly used graphics. A total of 252 UK residents from Prolific and 452 UK What Works Centre users interpreted the meaning of graphics shown without labels. Comprehension was low (often below 50%). The best-performing graphics combined unambiguous metaphorical shapes with color cues and indications of quantity. The participants also reported what types of evidence they wanted and in what detail (e.g., subgroups, different outcomes). Users particularly wanted to see intervention effectiveness and quality, and policymakers also wanted to know the financial costs and negative consequences. Comprehension and preferences were remarkably consistent between the two samples. Groups communicating evidence about policy options can use these results to design summaries, toolkits and reports for expert and non-expert audiences.

scholarly journals COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ye Emma Zohner ◽  
Jeffrey S. Morris
Keyword(s):  
Web Application ◽  
The United States ◽  
Mitigation Strategies ◽  
Time Lags ◽  
Policy Makers ◽  
Major Health ◽  
Dynamic Representation ◽  
Graphical Displays ◽  
The Usa ◽  
User Friendly

Abstract Background The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions. Findings COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe. Conclusion The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.

scholarly journals The Mode Heuristic in Service Consumers’ Interpretations of Online Rating Distributions

2021 ◽  
pp. 109467052110124
Author(s):  
Sarah Köcher ◽  
Sören Köcher
Keyword(s):  
Empirical Evidence ◽  
Graphical Display ◽  
Visual Salience ◽  
Graphical Displays ◽  
Volume Average ◽  
Online Rating ◽  
Process Level ◽  
Shed Light

In this article, the authors demonstrate a tendency among consumers to use the arithmetic mode as a heuristic basis when drawing inferences from graphical displays of online rating distributions in such a way that service evaluations inferred from rating distributions systematically vary by the location of the mode. The rationale underlying this phenomenon is that the mode (i.e., the most frequent rating which is represented by the tallest bar in a graphical display) attracts consumers’ attention because of its visual salience and is thus disproportionately weighted when they draw conclusions. Across a series of eight studies, the authors provide strong empirical evidence for the existence of the mode heuristic, shed light on this phenomenon at the process level, and demonstrate how consumers’ inferences based on the mode heuristic depend on the visual salience of the mode. Together, the findings of these studies contribute to a better understanding of how service customers process and interpret graphical illustrations of online rating distributions and provide companies with a new key figure that—aside from rating volume, average ratings, and rating dispersion—should be incorporated in the monitoring, analyzing, and evaluating of review data.

Graphical Displays for Understanding SEM Model Similarity

2017 ◽  
Vol 24 (6) ◽  
pp. 803-818 ◽  
Author(s):  
Keke Lai ◽  
Samuel B. Green ◽  
Roy Levy
Keyword(s):  
Graphical Displays ◽  
Model Similarity
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