Abstract
Meta-analysis is a probabilistic technique that combines results from several studies that approach the same topic and produce a result that sums up the whole. In the agricultural field, it is used to make empirical estimates of efficiency for the development of productivity and economic research on agriculture. Meta-analysis can be applied through software such as R, which is executed through commands, and produces results without providing user interactivity, nor does it reproduce a friendly and easy-to-understand interface. This paper presents the creation of a computer system, the WMA, which aims to simplify the execution of meta-analysis, providing a graphical interface and improves the display of the results through an interactive visualization using the Hierarchical Information Visualization Technique Bifocal Tree. For validation, the meta-analysis was applied in the agricultural area in a case study that grouped studies that used the fungicide fluquinconazole to combat the soybean rust disease, the results obtained through the application of the meta-analysis were analyzed using the WMA proposed tool.
Highlights
Meta-analysis (MA) combine and analyze the results of several experiments to better analyze the data
In order to combine the studies in a metaanalysis, it is necessary to determine the measure of effect (ME) and calculate it for each study
The nodes that are represented by the yellow colors on the graphic have no estimate of effect value because they are the nodes that represent the studies
Summary
Meta-analysis (MA) combine and analyze the results of several experiments to better analyze the data. R software operates through command lines previously known by the user and displays its results by means of statistical graphs. It does not provide interactivity with the user, nor does it provide a user-friendly interface that is easy to understand and operate. Some of the most used measures of effect for the calculation of the meta-analysis are (Borenstein et al 2009) standardized average difference, reason of risk, and correlation. The case study applied in this article uses the standardized mean difference effect measure
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