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

Review and Preview01:10

Review and Preview

6.9K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
6.9K
Data Validation01:03

Data Validation

4.8K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
4.8K
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

194
Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects...
194
Reliability and Validity01:29

Reliability and Validity

12.6K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.6K
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

1.3K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
1.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.1K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.1K

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

Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.

Behavior research methods·2026
Same author

Cross-Contextual Variability in Children's Early Understanding of Visual Media.

Topics in cognitive science·2025
Same author

Shifting Expectations for Encoding Rules Mitigates Misinterpretation of Connected Scatterplots.

IEEE transactions on visualization and computer graphics·2025
Same author

An Analysis of Text Functions in Information Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Humans Select Subgoals That Balance Immediate and Future Cognitive Costs During Physical Assembly.

Cognitive science·2025
Same author

Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework.

IEEE transactions on visualization and computer graphics·2025
Same journal

Self-face recognition under self-implicating threat: preserved self-prioritization and recalibrated control dynamics.

Cognitive research: principles and implications·2026
Same journal

Out of sight, out of mind? How discarded items shape environmental judgments.

Cognitive research: principles and implications·2026
Same journal

Implicit learning of social information in contextual cueing.

Cognitive research: principles and implications·2026
Same journal

A downside of conceptual metaphor: metaphoric alignments of black and white.

Cognitive research: principles and implications·2026
Same journal

Visual attention in bilingual instructional videos: effects of audiovisual congruency and subtitle language.

Cognitive research: principles and implications·2026
Same journal

Predicting accuracy in eyewitness showups: confidence and response time in the laboratory, confidence in the field.

Cognitive research: principles and implications·2026
查看所有相关文章

相关实验视频

Updated: May 17, 2025

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

638

评估两个数据可视化识字评估之间的趋同.

Erik Brockbank1,2, Arnav Verma3, Hannah Lloyd4

  • 1Department of Psychology, Stanford University, Stanford, USA. ebrockbank@stanford.edu.

Cognitive research: principles and implications
|April 5, 2025
PubMed
概括
此摘要是机器生成的。

两个数据可视化素养评估显示相关得分,但测量不同的技能. 需要进一步的研究来开发STEM教育的综合评估.

关键词:
数据素养数据素养图表理解能力 图表理解能力图形识字能力的提高心理测量评估是一种心理评估.对于STEM教育来说,这是非常重要的.

更多相关视频

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.2K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.8K

相关实验视频

Last Updated: May 17, 2025

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

638
Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
06:02

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Published on: October 6, 2020

2.2K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.8K

科学领域:

  • 在STEM教育方面.
  • 数据可视化 识字能力 数据可视化

背景情况:

  • 数据可视化对于理解定量数据至关重要.
  • 数据可视化素养是关键的教育目标.
  • 当前的评估可能无法完全捕捉数据可视化素养的构造.

研究的目的:

  • 调查两个常见的图形理解评估测量数据可视化素养相同的基础构造的程度.
  • 分析数据可视化素养评估中的个体变化和错误模式.

主要方法:

  • 向大学样本和人口统计学代表的美国成年人样本 (N=1,113) 进行了两次图形理解评估.
  • 分析了整体得分和个别错误模式,以比较评估结构.

主要成果:

  • 两项评估的总分是相关的,并且与之前的数学课程相关联.
  • 对错误模式的分析表明,评估探讨了数据可视化素养的不同组成部分.
  • 评估组件和测试的设计类别 (例如,值检索与比较) 之间没有明确的对应性.

结论:

  • 虽然总体得分一致,但当前的评估可能无法完全捕捉数据可视化素养的多面性质.
  • 研究结果表明,需要开发更全面的评估,更好地反映数据可视化理解中的行为模式.
  • 未来的评估应由组件组织起来,以更准确地代表数据可视化素养中的不同技能.