Understanding Bayesian reasoning via graphical displays

1989 ◽  
Vol 20 (SI) ◽  
pp. 381-386 ◽  
Author(s):  
W. G. Cole
Keyword(s):  
Bayesian Reasoning ◽  
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.

Information Use in Bayesian Reasoning

2014 ◽  
Author(s):  
Andrew Cohen ◽  
Adrian Staub ◽  
Jade Hedrick
Keyword(s):  
Information Use ◽  
Bayesian Reasoning

Understanding Bayesianism: Fundamentals for Process Tracers

2021 ◽  
pp. 1-8
Author(s):  
Andrew Bennett ◽  
Andrew E. Charman ◽  
Tasha Fairfield
Keyword(s):  
Social Science ◽  
Bayesian Analysis ◽  
Qualitative Methods ◽  
Recent Work ◽  
The State ◽  
Process Tracing ◽  
Bayesian Reasoning ◽  
Mutual Exclusivity ◽  
Positive View

Abstract Bayesian analysis has emerged as a rapidly expanding frontier in qualitative methods. Recent work in this journal has voiced various doubts regarding how to implement Bayesian process tracing and the costs versus benefits of this approach. In this response, we articulate a very different understanding of the state of the method and a much more positive view of what Bayesian reasoning can do to strengthen qualitative social science. Drawing on forthcoming research as well as our earlier work, we focus on clarifying issues involving mutual exclusivity of hypotheses, evidentiary import, adjudicating among more than two hypotheses, and the logic of iterative research, with the goal of elucidating how Bayesian analysis operates and pushing the field forward.

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.

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