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Related Concept Videos

Bar Graph01:07

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Best Graph Type to Compare Discrete Groups: Bar, Dot, and Tally.

Fang Zhao1, Robert Gaschler2

  • 1Research Cluster D2L2, FernUniversität in Hagen, Hagen, Germany.

Frontiers in Psychology
|January 10, 2022
PubMed
Summary

Graph schemas rely on common structures, not just visual features. Tally charts are best for comparing groups, outperforming bar graphs and requiring less processing time.

Keywords:
graph comprehensiongraph schemagraph typegroup comparisonmixing-costs paradigm

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Area of Science:

  • Cognitive Psychology
  • Human-Computer Interaction
  • Data Visualization

Background:

  • Graph comprehension and group comparison are influenced by underlying graph schemas.
  • Understanding whether these schemas are specific to each graph type or share common structures is crucial for effective data visualization.

Purpose of the Study:

  • To investigate if graph schemas are based on perceptual features or common invariant structures.
  • To determine the most effective graphic type (bar, dot, or tally chart) for comparing discrete groups.
  • To explore the impact of group position differences on processing time.

Main Methods:

  • Three experiments utilized the mixing-costs paradigm.
  • Participants compared quantities across three groups presented in randomized positions.
  • Data collection focused on mixing costs and processing times.

Main Results:

  • Graph schemas are based on common invariant structures, not solely perceptual features.
  • Mixing tally charts with bar or dot graphs incurred mixing costs, unlike pairing bar and dot graphs.
  • Tally charts proved more efficient for group comparison than bar graphs.
  • Increased positional difference between groups led to longer processing times.

Conclusions:

  • A unified, invariant structure underlies graph schemas, impacting comprehension.
  • Tally charts offer superior efficiency for discrete group comparisons.
  • Minimizing positional variance in graph design can enhance processing speed and user experience.