Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Bar Graph01:07

Bar Graph

16.0K
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...
16.0K
Review and Preview01:13

Review and Preview

8.9K
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...
8.9K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
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.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Ratio Level of Measurement00:54

Ratio Level of Measurement

17.5K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
17.5K
Pie Chart01:04

Pie Chart

13.8K
A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
In a pie chart, the central angle, the arc length of each slice, and the area are directly proportional to the quantity or percentage it represents. Some real-world examples that can be depicted using pie charts include marks obtained by students...
13.8K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

262
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
262

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Locatability and Locatability Robustness of Visual Variables in Single Target Localization.

IEEE transactions on visualization and computer graphics·2026
Same author

Running with Data: A Survey of the Current Research and a Design Exploration of Future Immersive Visualisations.

IEEE transactions on visualization and computer graphics·2025
Same author

Design Exploration of AI-Assisted Personal Affective Physicalization.

IEEE computer graphics and applications·2025
Same author

Correction: Pan et al. Lignin-Derived Activated Carbon as Electrode Material for High-Performance Supercapacitor. <i>Molecules</i> 2025, <i>30</i>, 89.

Molecules (Basel, Switzerland)·2025
Same author

Lignin-Derived Activated Carbon as Electrode Material for High-Performance Supercapacitor.

Molecules (Basel, Switzerland)·2025
Same author

Corncob-Derived Activated Carbon as Electrode Material for High-Performance Supercapacitor.

Materials (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jun 13, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

10.1K

The Effect of Visual Aids on Reading Numeric Data Tables.

Yongfeng Ji, Charles Perin, Miguel A Nacenta

    IEEE Transactions on Visualization and Computer Graphics
    |September 9, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Visual aids in data tables improve data reading. Zebra striping aids complex comparisons, while color and bars excel at identifying maximum values, enhancing data visualization effectiveness.

    More Related Videos

    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
    09:00

    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

    Published on: August 16, 2024

    722
    Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish
    14:43

    Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish

    Published on: July 18, 2020

    8.0K

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
    10:58

    Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

    Published on: January 2, 2011

    10.1K
    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
    09:00

    Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

    Published on: August 16, 2024

    722
    Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish
    14:43

    Universal Screening for Prevention of Reading, Writing, and Math Disabilities in Spanish

    Published on: July 18, 2020

    8.0K

    Area of Science:

    • Human-Computer Interaction
    • Data Visualization
    • Cognitive Psychology

    Background:

    • Data tables are a prevalent method for data presentation.
    • Visual elements in tables aim to improve readability, but empirical data is lacking.
    • Understanding how users interact with and perceive visual aids in tables is crucial.

    Purpose of the Study:

    • To investigate how different visual encodings in data tables affect user performance and behavior.
    • To address the empirical knowledge gap regarding table reading and the impact of visual aids.
    • To provide data-driven insights for designing more effective data tables.

    Main Methods:

    • A controlled study was conducted with participants performing four distinct tasks.
    • Four table representation conditions were used: plain, zebra striping, cell background color encoding, and in-cell bars.
    • Data collected included completion time, error rates, gaze-tracking, mouse movement, and participant preferences.

    Main Results:

    • Color and bar encodings significantly improved performance in identifying maximum values.
    • Zebra striping was more effective than color or bars for a complex task involving proportional difference comparisons.
    • Distinct user behaviors were characterized across the four tasks and table conditions.

    Conclusions:

    • The effectiveness of visual aids in data tables is task-dependent.
    • Design choices for data table visualization should consider the specific tasks users will perform.
    • Findings offer valuable guidance for optimizing data presentation and future research in data visualization.