Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Multiple Bar Graph

Multiple Bar Graph

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...

Time-Series Graph

Time-Series Graph

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...

Central Tendency: Analysis

Central Tendency: Analysis

Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...

Variability: Analysis

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

Relative Frequency Histogram

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...

You might also read

Related Articles

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

Sort by

Same author

Should extreme obesity in children be considered child abuse?

MCN. The American journal of maternal child nursing·2013

Same author

Should families be present during pediatric resuscitation?

MCN. The American journal of maternal child nursing·2012

Same author

Initial development and testing of a questionnaire of parental self-efficacy for enacting healthy lifestyles in their children.

Journal for specialists in pediatric nursing : JSPN·2012

Same author

Egocentric depth judgments in optical, see-through augmented reality.

IEEE transactions on visualization and computer graphics·2007

Same author

Evaluating human factors in augmented reality systems.

IEEE computer graphics and applications·2005

Same author

Moving mixed reality into the real world.

IEEE computer graphics and applications·2005

Search research articles

Related Experiment Video

Updated: May 28, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Evaluation of trend localization with multi-variate visualizations.

Mark A Livingston¹, Jonathan W Decker

¹Naval Research Laboratory, USA. mark.livingston@nrl.navy.mil

IEEE Transactions on Visualization and Computer Graphics

|October 29, 2011

Summary

This summary is machine-generated.

Evaluating multivariate visualization techniques is challenging. This study introduces "trend localization" to assess techniques like Brush Strokes and Dimensional Stacking, offering insights for data visualization research.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Related Experiment Videos

Last Updated: May 28, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Basics of Multivariate Analysis in Neuroimaging Data

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Area of Science:

Information Visualization
Human-Computer Interaction
Data Science

Background:

Multi-valued datasets with increasing dimensions are common.
Existing multivariate visualization techniques lack standardized evaluation methods.
Previous evaluations often use tasks not requiring multi-variable comparison.

Purpose of the Study:

To design and evaluate a novel task, "trend localization," for assessing multivariate visualization techniques.
To compare the effectiveness of five established multivariate visualization techniques against grayscale maps using the new task.

Main Methods:

Developed the "trend localization" task requiring multi-value comparison within visualizations.
Conducted a user study evaluating Brush Strokes, Data-Driven Spots, Oriented Slivers, Color Blending, and Dimensional Stacking.
Included juxtaposed grayscale maps as a baseline comparison.

Main Results:

Quantitative and qualitative results from the user study are presented.
Analysis of user performance across different visualization techniques and the baseline.
Discussion of the efficacy of each technique for the trend localization task.

Conclusions:

The "trend localization" task effectively highlights differences in multivariate visualization performance.
Findings provide implications for the design and selection of visualization techniques for complex datasets.
Recommendations are offered for future research in multivariate data visualization evaluation.