Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

83
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
83
Outliers and Influential Points01:08

Outliers and Influential Points

4.0K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
4.0K
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
Pareto Chart00:52

Pareto Chart

6.7K
A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
6.7K
Skewness01:06

Skewness

11.0K
The measures of central tendency calculated from a data set may not reveal much about its intrinsic distribution. If a plot is made of the data set’s values, the mean and the median may not only differ, but also the plot may have more values on one side of the central tendencies. Such a data set is said to be skewed towards that side.
The longer the tail of the plot on one side, the more skewed it is. The skewness of a data set’s values suggests that the measures of central tendency...
11.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Persistent input- and cell-type-specific synaptic alterations in the somatosensory thalamus of Dravet syndrome mice.

Journal of neurophysiology·2026
Same author

'They're waiting for us to break before they listen': healthcare workers' perspectives on how to mitigate moral distress in British Columbia, Canada.

Primary health care research & development·2026
Same author

Structure-Based Discovery of Potent BCL-XL Inhibitors through Rescaffolding.

Journal of medicinal chemistry·2026
Same author

Ebola: ignoring past lessons puts women at risk.

Lancet (London, England)·2026
Same author

Social Justice, Science Agency, and Collaborations: Using Participatory Science to Enhance Learning Outcomes in Postsecondary STEM Courses.

CBE life sciences education·2026
Same author

Comparative assessment of RT-dPCR and RT-qPCR for RNA quantification in IgG mRNA-lipid nanoparticle bioanalysis.

Bioanalysis·2026

相关实验视频

Updated: Jun 12, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.5K

想象不平等:迈向公平学生成绩的一步

Sumitra Tatapudy1, Rachel Potter1, Linnea Bostrom1

  • 1Department of Biology, University of Washington, Seattle, WA 98195.

CBE life sciences education
|September 25, 2024
PubMed
概括
此摘要是机器生成的。

一个R-Shiny应用程序有助于教育工作者可视化学生绩效数据,以识别和解决科学,技术,工程和数学 (STEM) 教育的机会差距,以帮助代表性不足的群体.

更多相关视频

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
Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

23.6K

相关实验视频

Last Updated: Jun 12, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.5K
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
Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
14:32

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

23.6K

科学领域:

  • 教育技术的教育技术.
  • 在STEM教育研究研究中.
  • 高等教育中的公平.

背景情况:

  • 持续存在的不平等存在于低收入,第一代,性别少数群体,黑人,拉丁裔和土著学生在科学,技术,工程和数学 (STEM).
  • 在STEM课程中的机会差距导致这些学生群体的代表性不足和表现不佳.
  • 打破这些不平等需要政策和实践,以解决系统性劣势.

研究的目的:

  • 开发一个工具,促进教师和机构对学生表现的数据知情反思.
  • 为了使STEM教育中的不平等模式更加可见和可访问.
  • 支持创建变革性和公平的教育经验.

主要方法:

  • 开发一个R-Shiny应用程序用于可视化学生绩效数据.
  • 使用公开检索的数据作为一个说明性的例子.
  • 提供免费可用的代码以适应本地数据源.

主要成果:

  • R-Shiny应用程序允许认证用户可视化学生表现中的不平等.
  • 该工具可以被个人教师,团体和机构用于自我反思.
  • 附带的代码可以适应本地数据集成.

结论:

  • 想象机会差距是创造公平STEM教育的关键一步.
  • 开发的R-Shiny应用程序及其代码为教育工作者和机构提供了一个实用的解决方案.
  • 鼓励自我反思和围绕公平数据的讨论可以推动高等教育的变革性变化.