Use Of Graphs Research Articles

Creating Single-Subject Research Design Graphs with Google Applications.

The visual analysis of graphically displayed data is an essential skill for behavior analysts across a range of settings. Although there are several computer-based applications that facilitate the production of simple, consistent, and visually coherent graphs, these applications have several limitations, including cost. An alternative to using these applications is using free and widely available Google Sheets and Google Slides to produce high-quality clinical and research graphs. We provide a step-by-step pictorially supported task analysis for a system for creating graphs for a variety of single-subject research designs and clinical applications using Sheets and Slides. We also discuss the advantages and limitations of using Google applications to create graphs for use in the practice of applied behavior analysis.

EPOS2020 and beyond

The April issue of Rhinology includes the Executive Summary of EPOS2020, highlighting the most important up-dates on disease definitions, treatment algorithms and integrated care pathway approaches of CRS. EPOS2020 (1) is the result of major efforts by a team of passionate rhinologists and associate health care providers coming from all corners of the world coordinated by Professors Wytske Fokkens and Valerie Lund. The Executive Summary presented here illustrates the most important graphs for use in daily practice and teaching purposes. In addition, the April issue of Rhinology also brings relevant CRS-related aspects to the attention of the readers of Rhinology.

EPOS2020 and beyond

The April issue of Rhinology includes the Executive Summary of EPOS2020, highlighting the most important up-dates on disease definitions, treatment algorithms and integrated care pathway approaches of CRS. EPOS2020 (1) is the result of major efforts by a team of passionate rhinologists and associate health care providers coming from all corners of the world coordinated by Professors Wytske Fokkens and Valerie Lund. The Executive Summary presented here illustrates the most important graphs for use in daily practice and teaching purposes. In addition, the April issue of Rhinology also brings relevant CRS-related aspects to the attention of the readers of Rhinology.

EPOS2020 and beyond.

The April issue of Rhinology includes the Executive Summary of EPOS2020, highlighting the most important up-dates on disease definitions, treatment algorithms and integrated care pathway approaches of CRS. EPOS2020 (1) is the result of major efforts by a team of passionate rhinologists and associate health care providers coming from all corners of the world coordinated by Professors Wytske Fokkens and Valerie Lund. The Executive Summary presented here illustrates the most important graphs for use in daily practice and teaching purposes. In addition, the April issue of Rhinology also brings relevant CRS-related aspects to the attention of the readers of Rhinology.

The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews.

Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we integrated and customised two existing JavaScript libraries to create a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison with participants' standard practice for extracting data from graphs in PDF documents. Results: We found that the customised graphical data extraction tool is not inferior to users' (N=10) prior standard practice. Our study was not designed to show superiority, but suggests that, on average, participants saved around 6 minutes per graph using the new tool, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews

Background:The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process.Methods:To mitigate some of the above problems we integrated and customised two existing JavaScript libraries to create a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison to standard practice.Results:We found that the customised graphical data extraction tool is not inferior to users’ prior preferred current approaches. Our study was not designed to show superiority, but suggests that there may be a saving in time of around 6 minutes per graph, accompanied by a substantial increase in accuracy.Conclusions:Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews

Background: The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process. Methods: To mitigate some of the above problems we integrated and customised two existing JavaScript libraries to create a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison with participants’ standard practice for extracting data from graphs in PDF documents. Results: We found that the customised graphical data extraction tool is not inferior to users’ (N=10) prior standard practice. Our study was not designed to show superiority, but suggests that, on average, participants saved around 6 minutes per graph using the new tool, accompanied by a substantial increase in accuracy. Conclusions: Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews

Background:The extraction of data from the reports of primary studies, on which the results of systematic reviews depend, needs to be carried out accurately. To aid reliability, it is recommended that two researchers carry out data extraction independently. The extraction of statistical data from graphs in PDF files is particularly challenging, as the process is usually completely manual, and reviewers need sometimes to revert to holding a ruler against the page to read off values: an inherently time-consuming and error-prone process.Methods:To mitigate some of the above problems we developed a new web-based graphical data extraction tool to assist reviewers in extracting data from graphs. This tool aims to facilitate more accurate and timely data extraction through a user interface which can be used to extract data through mouse clicks. We carried out a non-inferiority evaluation to examine its performance in comparison to standard practice.Results:We found that our new graphical data extraction tool is not inferior to users’ prior preferred current approaches. Our study was not designed to show superiority, but suggests that there may be a saving in time of around 6 minutes per graph, accompanied by a substantial increase in accuracy.Conclusions:Our study suggests that the incorporation of this type of tool in online systematic review software would be beneficial in facilitating the production of accurate and timely evidence synthesis to improve decision-making.

Adaptation of Chain Event Graphs for use with Case-Control Studies in Epidemiology.

Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditional analyses report usually only the odds ratio for given exposures and the binary disease status. Chain event graphs are a graphical representation of a statistical model derived from event trees which have been developed in artificial intelligence and statistics, and only recently introduced to the epidemiology literature. They are a modern Bayesian technique which enable prior knowledge to be incorporated into the data analysis using the agglomerative hierarchical clustering algorithm, used to form a suitable chain event graph. Additionally, they can account for missing data and be used to explore missingness mechanisms. Here we adapt the chain event graph framework to suit scenarios often encountered in case-control studies, to strengthen this study design which is time and financially efficient. We demonstrate eight adaptations to the graphs, which consist of two suitable for full case-control study analysis, four which can be used in interim analyses to explore biases, and two which aim to improve the ease and accuracy of analyses. The adaptations are illustrated with complete, reproducible, fully-interpreted examples, including the event tree and chain event graph. Chain event graphs are used here for the first time to summarise non-participation, data collection techniques, data reliability, and disease severity in case-control studies. We demonstrate how these features of a case-control study can be incorporated into the analysis to provide further insight, which can help to identify potential biases and lead to more accurate study results.

Graphical Reporting in Italian Annual Reports during the Financial Crisis: Impression Management or Incremental Information?

This paper investigates whether, before and during the global financial crisis, Italian firms have used financial key performance indicators graphs in the annual reports as impression management tools, to portray a more favourable picture of the firm's performance than is warranted. This study shows that, during the financial crisis, firms have increased the number of graphs and decreased favourable distortions, although graphs continued to be designed inaccurately. The findings could reflect an increased public scrutiny on the firm's performance, during the financial crisis. As a theoretical implication, this paper contributes to the existent financial reporting literature by showing that graphs are not necessarily used in line with an agency theory-based impression management, which is the dominant perspective to explain the graphs' usage in the annual reports during periods of performance upturn. Moreover, it shows that the institutional context can affect voluntary disclosure practices at a firm-level. As a practical implication, this study suggests to annual reports' readers not to necessarily consider managers as self-serving preparers in their graphical reporting strategies. The study also suggests accounting associations, audit firms and other regulatory bodies to create a set of guidelines for a correct graph's use and design.

Neighborhood Failures in Random Topology Covert Communication Networks

A covert communications network is a connected, overlay, peer-to-peer network being used to support communications within a group in which the survival of the group depends on (a) confidentiality and anonymity for communications, (b) concealment of participation in the network to both other members of the group and external eavesdroppers, and (c) resilience against disconnec- tion. These requirements are much more stringent than for typical privacy and anonymity systems. In order to protect the network from subversion of individual nodes, neighbors of failed nodes have to be isolated from the network as well. Thus, all failures have to treated as neighborhood failures. Network membership is protected through topology management in order to limit each participant's knowledge of the network addresses (IP address, email address, etc.) of other participants. In this paper, we propose a measure for determining the suitability of random topologies for use in a covert communication network, and we use this measure to analyze the suitability of Erdös-Rényi Random Graphs for use in a covert communication network.

Causal inference and developmental psychology.

Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether the risk factor actually causes outcomes. Random assignment is not possible in many instances, and for that reason, psychologists must rely on observational studies. Such studies identify associations, and causal interpretation of such associations requires additional assumptions. Research in developmental psychology generally has relied on various forms of linear regression, but this methodology has limitations for causal inference. Fortunately, methodological developments in various fields are providing new tools for causal inference-tools that rely on more plausible assumptions. This article describes the limitations of regression for causal inference and describes how new tools might offer better causal inference. This discussion highlights the importance of properly identifying covariates to include (and exclude) from the analysis. This discussion considers the directed acyclic graph for use in accomplishing this task. With the proper covariates having been chosen, many of the available methods rely on the assumption of "ignorability." The article discusses the meaning of ignorability and considers alternatives to this assumption, such as instrumental variables estimation. Finally, the article considers the use of the tools discussed in the context of a specific research question, the effect of family structure on child development.

ROC graphs with instance-varying costs

Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs have been used in cost-sensitive learning because of the ease with which class skew and error cost information can be applied to them to yield cost-sensitive decisions. However, they have been criticized because of their inability to handle instance-varying costs; that is, domains in which error costs vary from one instance to another. This paper presents and investigates a technique for adapting ROC graphs for use with domains in which misclassification costs vary within the instance population.

Constructing single-subject reversal design graphs using Microsoft Excel™: A comprehensive tutorial.

The single-subject reversal design graph is a critical element to basic research and applied behavior analysis. This technical tutorial gives thorough instructions for creating a reversal graph with most recent versions of Word including Word for Windows 2003 and for Mac 2004. Directions for use with prior versions of this program are included. Following these step-by-step instructions will yield graphs that conform to the Manuscript Preparation Checklist in the Journal of Applied Behavior Analysis (2000). Key words: single-subject reversal design, MS Word (tm) graphs, tutorial ********** Graphic representation of repeated measurement data is critical in basic research and applied behavior analysis. The purpose of this tutorial is to produce single subject design (SSD) graphs using Word (tm) while conforming to the Manuscript Preparation Checklist (Journal of Applied Behavior Analysis, 2000). (1) Word is an excellent, ubiquitous word processing program and has several features that make it well-suited to create graphs. For example, it has built-in flexible graphing features, and the resulting diagrams can be used in presentations, printed for journal submission, or posted into presentation program such as PowerPoint. Furthermore, by following the directions in this tutorial, it is possible to save templates, allowing the reader to create multiple SSD graphs while only changing data points and labels. Using templates lessens the amount of formatting needed to properly produce SSD graphs. Excel also provides graphing abilities (Carr & Burkholder, 1998; Moran & Hirschbine, 2002), but using the features built into Word saves time and reduces the issues related to converting graphs for use in word processing or presentation software. Both Word 2003 for Windows and Word 2004 for Macintosh are covered in this tutorial. These directions also apply to prior versions; however, each one includes minor variations in procedure. When possible, comments are included to allow users of prior versions to successfully complete these steps. This tutorial assumes the reader has a working knowledge of their operating system, but no experience with Word's charting features. Remember to save your work often while developing a SSD graph. Starting the Word Document for Graphing To begin production of a SSD graph, start with an open Word document. Click on the Insert menu at the top of the screen and select the menu item labeled ... A dialog box of options titled Object will appear. Under the file tab Create New, scroll down the list and click on item labeled Microsoft and click OK. Note that Word refers to graphs as charts. A separate window entitled Datasheet will appear. Sample data is included in the spreadsheet and by default the chart contains a bar graph of the sample data. You will be replacing the sample data and reformatting the graph, but before doing so, it is necessary to become oriented to how Word handles chart editing. Orienting to the Chart Editing Environment Word is a multifunction program, and chart editing is handled separately from word processing. An application named Microsoft Graph handles chart editing. Different versions of Word vary with how explicit these two environments are from each other. For example, with recent Windows PC versions, is fully integrated within the Word window so that when a chart is selected, the relevant tools and menus appear in Word. When you click away from the chart to deselect it, the menus and buttons go back to those intended to edit text. Double clicking the chart area engages chart editing capabilities. In contrast, on the Mac, is launched automatically as a separate application when the user double clicks the chart. Once the reader has finished editing the graph, quitting the application on the Mac returns the user into Word to edit the text. …

Analysis of the autonomous system network topology

Mapping the Internet is a major challenge for network researchers. It is the key to building a successful modeling tool able to generate realistic graphs for use in networking simulations. In this paper we provide a detailed analysis of the inter-domain topology of the Internet. The collected data and the resulting analysis began in November 1997 and cover a period of two and a half years. We give results concerning major topology properties (nodes and edges number, average degree and distance, routing policy, etc.) and main distributions (degree, distance, etc.). We also present many results about the trees of this network. The evolution of these properties is reviewed and major trends are highlighted. We propose some empirical laws that match this current evolution. Four new power-laws concerning the number of shortest paths between node pairs and the tree size distribution are provided with their detailed validation.

On the size of a random sphere of influence graph

We approach sphere of influence graphs (SIGs) from a probabilistic perspective. Ordinary SIGs were first introduced by Toussaint as a type of proximity graph for use in pattern recognition, computer vision and other low-level vision tasks. A random sphere of influence graph (RSIG) is constructed as follows. Consider n points uniformly and independently distributed within the unit square in d dimensions. Around each point, Xi, draw an open ball (‘sphere of influence’) with radius equal to the distance to Xi's nearest neighbour. Finally, draw an edge between two points if their spheres of influence intersect. Asymptotically exact values for the expected number of edges in a RSIG are determined for all values of d; previously, just upper and lower bounds were known for this quantity. A modification of the Azuma-Hoeffding exponential inequality is employed to exhibit the sharp concentration of the number of edges around its expected value.

On the size of a random sphere of influence graph

We approach sphere of influence graphs (SIGs) from a probabilistic perspective. Ordinary SIGs were first introduced by Toussaint as a type of proximity graph for use in pattern recognition, computer vision and other low-level vision tasks. A random sphere of influence graph (RSIG) is constructed as follows. Consider n points uniformly and independently distributed within the unit square in d dimensions. Around each point, X i , draw an open ball (‘sphere of influence’) with radius equal to the distance to X i 's nearest neighbour. Finally, draw an edge between two points if their spheres of influence intersect. Asymptotically exact values for the expected number of edges in a RSIG are determined for all values of d; previously, just upper and lower bounds were known for this quantity. A modification of the Azuma-Hoeffding exponential inequality is employed to exhibit the sharp concentration of the number of edges around its expected value.

Creating effective graphs and charts

Reviews some of the aspects of the creation of effective graphs. Illustrates key principles of good chart design using a variety of examples. Acquaintance with these concepts should support managers in the creation of effective charts and graphs for use in documents and presentations.

Approximation techniques for hypergraph partitioning problems

Techniques for approximating a hypergraph by a weighted graph for use in node partitioning algorithms are described. The graphs use the same node set as the hypergraph and their edges are obtained by generating a series of cliques that correspond to subsets of the hyper edges. Edge weights are obtained by characterizing optimal solutions to mathematical programs that describe properties of feasible partitions of the hypergraph. These approximations are compared with other known approximations and yield promising results.

An efficient representation for sparse sets

Sets are a fundamental abstraction widely used in programming. Many representations are possible, each offering different advantages. We describe a representation that supports constant-time implementations of clear-set, add-member, and delete-member . Additionally, it supports an efficient forall iterator, allowing enumeration of all the members of a set in time proportional to the cardinality of the set. We present detailed comparisons of the costs of operations on our representation and on a bit vector representation. Additionally, we give experimental results showing the effectiveness of our representation in a practical application: construction of an interference graph for use during graph-coloring register allocation. While this representation was developed to solve a specific problem arising in register allocation, we have found it useful throughout our work, especially when implementing efficient analysis techniques for large programs. However, the new representation is not a panacea. The operations required for a particular set should be carefully considered before this representation, or any other representation, is chosen.