Application of Hierarchical Visualization Techniques in Meta-Analysis Data

Application of Hierarchical Visualization Techniques in Meta-Analysis Data

Bruna Rossetto Delazeri (Department of Informatics, State University of Ponta Grossa, Ponta Grossa, Brazil), Felipe Paes Gusmão (Federal Technological University of Parana, Ponta Grossa, Brazil), Simone Nasser Matos (Department of Computer Science, Federal Technological University of Parana, Ponta Grossa, Brazil), Alaine Margarete Guimarães (Department of Computer Science, State University of Ponta Grossa, Ponta Grossa, Brazil) and Marcelo Giovanetti Canteri (Department of Agronomy, Londrina State University, Londrina, Brazil)
DOI: 10.4018/IJAEIS.2018010101
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Abstract

The meta-analysis is a probabilistic technique that groups the results of several studies, approaches the same subject and produces a result that summarizes the whole. The results that are displayed in graphical form neither offer interactivity with the user, nor a user-friendly interface and easy comprehension. In order to obtain a visual exploratory analysis with more satisfactory results, there are information visualization techniques applied to map the data in graphical form to broaden the user cognition. This article performs the execution of the meta-analysis, through R software, in order to determine the efficiency of fungicide fluquinconazole when combating Asian soy rust and applies the Technique for the Visualization of Hierarchical Information Structure; the Bifocal Tree, to improve the results displayed by the R through the forest plot graphic.
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Meta-Analysis

The meta-analysis uses grouping statistical techniques by combining and analyzing the results of several studies from an area in order to obtain more specific or even definitive results. Regardless the literature reviews that performs a descriptive work (Patel, 1989). The most important prerequisite of meta-analysis is that the various individual studies approach the same research topic or examine different aspects of a broader issue. The goals of a meta-analysis can be defined by Patel (1989):

  • Get an ideal central estimate of a series of quantitative estimates.

  • Improve the accuracy with which a quantity is estimated.

  • Resolve an uncertain matter when a series of studies differ in their conclusions.

  • Give answers to questions that are not treated in any individual study, which can be examined in subgroups of systematic studies in different settings.

Although some authors (Feuer, 1989; Delahaye et al., 1991) only approach quantitative estimates, resulting from several studies statistical calculations, Delahayne et al. (1991) explain that both the quantitative and the qualitative approaches are possible in the meta-analysis. Methodological analysis identifies studies which have scientific validity and provides the original research with a qualitative answer (Venables, 2001). The steps of a qualitative approach carried out through meta-analysis are (Delahaye et al., 1991):

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