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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.5K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.5K
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test01:09

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

7.0K
In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with...
7.0K
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

4.2K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
4.2K
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

4.2K
A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
4.2K
Measures of Intelligence01:29

Measures of Intelligence

8.7K
Psychologists measure intelligence by using standardized tests that produce a score known as the intelligence quotient or IQ. To understand IQ tests, it's important to recognize the key principles behind their construction: validity, reliability, and standardization.
Validity refers to how well a test measures what it claims to measure. An intelligence test should accurately assess intelligence rather than another characteristic, like anxiety. Criterion validity is one way to evaluate this;...
8.7K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

513
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
513

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

Monitoring the safety of the adjuvanted human papillomavirus vaccine HPV-16/18-AS04 in association with pregnancy and potential immune-related diseases in China: A retrospective observational cohort study.

Human vaccines & immunotherapeutics·2026
Same author

Preserving Airway Reflexes During Awake Intubation: A Cautionary Note on Deep Sedation Protocols [Response to Letter].

Drug design, development and therapy·2026
Same author

Analyzing Complex Educational Data: A Data Analytic Framework for Integrating Structured and Unstructured Eye-Tracking Data.

Psychometrika·2026
Same author

m6AHD: a new framework for identifying abnormal N6-methyladenosine (m6A) in heart diseases based on sequencing features.

Frontiers in genetics·2026
Same author

Enhanced holographic polymer-dispersed liquid crystal gratings through RAFT photopolymerization.

Optics letters·2026
Same author

ENPP2 Protects Mouse Myocardium from Ischemia-Reperfusion-Induced Ferroptosis Injury Via the SIRT1/PGC-1α/NRF1 Pathway.

Applied biochemistry and biotechnology·2026
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
Same journal

On dimensional implication graphs.

Psychometrika·2026
查看所有相关文章

相关实验视频

Updated: Feb 26, 2026

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging
10:10

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging

Published on: January 30, 2026

332

试管婴儿的诊断分类模型:方法和理论

Xin Xu1, Guanhua Fang2, Jinxin Guo3

  • 1Beijing Normal University.

Psychometrika
|February 25, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的诊断分类模型 (DCM),该模型考虑了教育评估中属性配置文件和测试卷效应之间的相关性. 与现有方法相比,改进的模型显示了更好的适应性.

关键词:
在 PISA 测试中,这就是Q矩阵.诊断分类模型的诊断分类模型假设测试 测试 假设测试可以识别的可识别性互动互动互动互动互动.模型选择,模型选择.测试小组 DINA 的测试小组.

更多相关视频

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

1.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

相关实验视频

Last Updated: Feb 26, 2026

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging
10:10

Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging

Published on: January 30, 2026

332
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

1.3K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

科学领域:

  • 教育测量教育的测量
  • 心理测量建模 心理测量建模
  • 隐性变量分析 隐性变量分析

背景情况:

  • 诊断分类模型 (DCM) 对于形成性评估至关重要.
  • 试卷响应理论 (TRT) 模型,就像试卷DINA (T-DINA) 一样,结合了项目分组效应.
  • 现有的T-DINA模型假设属性配置文件和测试小组效应之间的独立性.

研究的目的:

  • 扩展T-DINA模型,允许属性配置文件和测试卷效应之间的相关性.
  • 调查拟议的扩展T-DINA模型的可识别性.
  • 用现实世界的评估数据来评估模型的性能.

主要方法:

  • 开发一个扩展的试管婴儿DINA (T-DINA) 模型,其中包含相关的潜伏结构.
  • 模型可识别性的理论分析,建立足够的条件.
  • 该模型应用于2015年国际学生评估计划 (PISA) 数据集.
  • 与标准DINA和T-DINA模型进行比较分析.
  • 模拟研究用于评估不同条件下的模型性能.

主要成果:

  • 与DINA和标准T-DINA相比,提议的扩展T-DINA模型显示了与DINA和标准T-DINA相比,适合度的显著改善.
  • 建立了足够的条件来识别扩展模型.
  • 标准T-DINA模型的可识别性也被证实为次要结果.
  • 该模型在不同环境的模拟研究中表现出强的性能.

结论:

  • 扩展的T-DINA模型为教育和心理测量中复杂的数据结构提供了更准确的表示.
  • 考虑到属性配置文件和测试卷效应之间的相关性,可以提高模型的合适性,并提供更深入的见解.
  • 这些发现支持使用这种先进的DCM来改进形成性评估和数据分析.