Double Bar Graphs | ScienceGate

Este trabajo muestra los resultados alcanzados durante la búsqueda de patrones ocultos, aplicando algoritmos estadísticos, a una base de datos bibliográfica. Para esta investigación se seleccionó el software WinIDAMS v.1.3, que utiliza para el manejo de los datos la construcciónde un dataset IDAMS (BUILD) y la agrupación de datos (AGGREG), para el análisis estadístico los algoritmos Análisis de conglomerados (CLUSFIND) y los diagramas de dispersión (SCAT). Para las salidas de los resultados este software ofrece las tablas multidimensionales, capaces de crear por cada grupo de variables seleccionadas una tabla interna con resultados como la frecuencia y la media aritmética, que fueron las seleccionadas para estas pruebas, mientras que para la representación gráfica de los resultados se decidió utilizar los histogramas porque son gráficas de barras que permiten interpretar de forma muy fácil y rápida el comportamiento de las variables seleccionadas para el análisis. Este estudio encontró patrones a través de la clusterización con los cuales fue posible potenciar los servicios de difusión selectiva de la información y proponer nuevos servicios para que formen parte de los productos que brinda la biblioteca. This work shows the results obtained during the search for hidden patterns using statistical algorithms, in a bibliographic database. For this research the WinIDAMS v.1.3 software, used for data management, building an IDAMS dataset (BUILD) and Data Group (AGGREG) for statistical analysis algorithms, Cluster analysis was selected (CLUSFIND) and scatterplots (SCAT). In addition to the outputs of the results this software provides multidimensional tables, able to create for each group of selected variables, an internal table with results such as frequency and average arithmetic were selected for these tests, while for graphical representation of theresults, it was decided to use histograms, because they are bar graphs that allow us to interpret very easily and quickly, the behavior of the variables selected for analysis. This study found patterns through clustering, with which services could enhance Selective Dissemination of Information and propose new services, to become part of the products offered by the library.

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Perceptions of Auditor Negligence: The Effects of Big Data Visualisations on Jurors’ Decisions

10.26686/wgtn.17144102 ◽

2021 ◽

Author(s):

◽

Travis Christensen

Keyword(s):

Social Media ◽

Big Data ◽

Data Collection ◽

Jury Verdicts ◽

Word Clouds ◽

Audit Litigation ◽

Different Types ◽

Different Sources ◽

Bar Graphs

<p>This study analyses the effects of Big Data visualisations on jurors’ decisions in audit litigation cases. Specifically, the study investigates the effects of different types of Big Data visualisations (word clouds or bar graphs) and different sources of Big Data (emails or social media posts) on jurors’ perceptions of auditors’ work and the size of the negligence awards that jurors recommend. The study theorises that the emotions elicited and the reliability of the data used to create visualisations such as word clouds will have dramatic effects on jury verdicts in audit negligence trials. There is considerable literature to support this assertion. However, after data collection, it was discovered that jurors are not influenced by the emotions elicited by visualisations. Rather, participants were very sceptical of more novel types of visualisations, such as word clouds, but could be persuaded by the inherent emotions elicited and the reliability of the data if they found the visualisation useful.</p>

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A Componential Model of Human Interaction with Graphs. H. Effects of the Distances among Graphical Elements

Proceedings of the Human Factors Society Annual Meeting ◽

10.1177/154193129203600422 ◽

1992 ◽

Vol 36 (4) ◽

pp. 365-368 ◽

Cited By ~ 4

Author(s):

Douglas J. Gillan ◽

Michael Neary

Keyword(s):

Response Times ◽

Spatial Relations ◽

Human Interaction ◽

Empirical Validation ◽

Arithmetic Operations ◽

Component Processes ◽

Bar Graphs

Based on task analyses of people using graphs, Gillan and Lewis (1992) have developed a model that describes how people interact with graphs. The model proposes that for simple tasks (e.g., comparisons and subtraction) and common graphs (e.g., line, scatter, and bar graphs), graph users apply combinations of five component processes — Searching for indicators, Encoding the value of indicators, performing Arithmetic Operations on the values, making Spatial Comparisons among the indicators, and Responding. The model further suggests that the combination and order of the components that the user applies depends on a user's task and the type of graph. The present research investigated two predictions from the model concerning spatial relations in a graph: (1) that response times to answer comparison questions should be sensitive to varying the distance between two indicators, but not to varying the indicator-to-axis distance, and (2) that response times to answer difference questions should be sensitive to the distance between the indicator and the y-axis, but not to the distance between the indicators. In the experiment, subjects used line and bar graphs to answer comparison and difference questions in which the appropriate distances varied systematically. The results of the research supported both predictions, thereby providing empirical validation of the model. In addition, some aspects of the data were not anticipated by the model, suggesting the need to enhance the componential model.

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Eggsactly How Many?

Teaching Children Mathematics ◽

10.5951/teacchilmath.22.9.0526 ◽

2016 ◽

Vol 22 (9) ◽

pp. 526-528 ◽

Cited By ~ 1

Keyword(s):

Problem Solvers ◽

Analyze Data ◽

Bar Graphs

Each month, this section of the Problem Solvers department showcases students' in-depth thinking and discusses the classroom results of using problems presented in previous issues of Teaching Children Mathematics. During this particular investigation, students have the opportunity to explore the structure of pictographs and bar graphs and to examine, compare, and analyze data.

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Interpreting & Predicting from Bar Graphs

Australasian Journal of Early Childhood ◽

10.1177/183693919902400206 ◽

1999 ◽

Vol 24 (2) ◽

pp. 22-27 ◽

Cited By ~ 4

Author(s):

Jane M. Watson ◽

Jonathan B. Moritz

Keyword(s):

Bar Graphs

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Frederick H. Sterns and the Portrayal of Variation in Central Plains Pottery

American Antiquity ◽

10.1017/s0002731600042311 ◽

2008 ◽

Vol 73 (2) ◽

pp. 361-370

Author(s):

Donna C. Roper

Keyword(s):

Culture Change ◽

Recent History ◽

Clinal Variation ◽

Late Prehistoric ◽

Tabular Data ◽

Central Plains ◽

History Of ◽

Neck Diameter ◽

Data Summaries ◽

Bar Graphs

Lyman et al.’s recent history of graphic depictions of culture change attributes the first use of bar graphs to James Ford in 1935. Ford, though, was anticipated in 1915 by Frederick Sterns, working with pottery from 27 late prehistoric Nebraska phase lodge sites in eastern Nebraska. Sterns used both tabular data summaries and divided bar graphs to show ordered variation over space in vessel neck diameter, types of appendages, and type of decoration. Underlying this analysis was a conception of these dimensions as varying independently of one another. Geographic groups within the Nebraska phase therefore exhibit clinal variation and can be characterized by differing proportions of attributes. Sterns’s work never became very well-known as archaeologists on the Central Plains turned to typological analysis for organizing pottery assemblages.

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Reveal, Don’t Conceal

Circulation ◽

10.1161/circulationaha.118.037777 ◽

2019 ◽

Vol 140 (18) ◽

pp. 1506-1518 ◽

Cited By ~ 10

Author(s):

Tracey L. Weissgerber ◽

Stacey J. Winham ◽

Ethan P. Heinzen ◽

Jelena S. Milin-Lazovic ◽

Oscar Garcia-Valencia ◽

...

Keyword(s):

Systematic Review ◽

Study Design ◽

Scientific Literature ◽

Continuous Data ◽

Summary Statistics ◽

Peripheral Vascular ◽

Box Plots ◽

Common Problems ◽

Mixed Messages ◽

Bar Graphs

Reports highlighting the problems with the standard practice of using bar graphs to show continuous data have prompted many journals to adopt new visualization policies. These policies encourage authors to avoid bar graphs and use graphics that show the data distribution; however, they provide little guidance on how to effectively display data. We conducted a systematic review of studies published in top peripheral vascular disease journals to determine what types of figures are used, and to assess the prevalence of suboptimal data visualization practices. Among papers with data figures, 47.7% of papers used bar graphs to present continuous data. This primer provides a detailed overview of strategies for addressing this issue by (1) outlining strategies for selecting the correct type of figure depending on the study design, sample size, and the type of variable; (2) examining techniques for making effective dot plots, box plots, and violin plots; and (3) illustrating how to avoid sending mixed messages by aligning the figure structure with the study design and statistical analysis. We also present solutions to other common problems identified in the systematic review. Resources include a list of free tools and templates that authors can use to create more informative figures and an online simulator that illustrates why summary statistics are meaningful only when there are enough data to summarize. Last, we consider steps that investigators can take to improve figures in the scientific literature.

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Integral and Separable Dimensions in Graph Reading

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181397041002138 ◽

1997 ◽

Vol 41 (2) ◽

pp. 1352-1356

Author(s):

J. G. Hollands ◽

Ian Spence

Keyword(s):

Graph Reading ◽

Bar Graphs

Subjects sorted decks of cards depicting pie charts and divided bar graphs on two criteria: the proportion shown in the graph, and the graph's overall size, or scaling. Sorting times and errors were measured. For divided bars, performance was impaired when subjects were required to sort the proportion and the overall scaling varied. No such impairment occurred for pie charts. The results suggest that proportion and scaling are integral dimensions for divided bar graphs, but separable dimensions for pie charts. Subjects can judge angles or slopes with pie charts having different scaling, but must estimate a ratio prior to classification with different-scale divided bars. In sum, showing proportions with divided bar graphs can be problematic if the scaling of the graph varies, but pie charts are not similarly affected.

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