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

Interval Level of Measurement00:55

Interval Level of Measurement

17.9K
For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
17.9K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

672
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
672
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

9.2K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
9.2K
Prediction Intervals01:03

Prediction Intervals

3.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.2K
Confidence Intervals01:21

Confidence Intervals

10.0K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
10.0K
Midrange01:07

Midrange

4.2K
A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
4.2K

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

Multinomial models of the repetition-based truth effect: Investigating the role of prior knowledge.

Memory & cognition·2026
Same author

Extensions of multinomial processing tree models for continuous variables: A simulation study comparing parametric and non-parametric approaches.

Behavior research methods·2025
Same author

Exploring the predictive value of different affect dynamics for psychological treatment outcome.

Psychological assessment·2025
Same author

Threat expectancies in a VR fear conditioning paradigm follow non-linear extinction patterns but are not influenced by intolerance of uncertainty.

Scientific reports·2025
Same author

Associations between ecological momentary assessment and passive sensor data in a large student sample.

Journal of psychopathology and clinical science·2025
Same author

Modeling the link between the plausibility of statements and the truth effect.

Psychonomic bulletin & review·2025
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: Jan 12, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.8K

间隔共识模型:聚合连续约束间隔响应.

Matthias Kloft1, Björn S Siepe1, Daniel W Heck1

  • 1Department of Psychology, https://ror.org/01rdrb571Philipps-Universität Marburg, Germany.

Psychometrika
|November 4, 2025
PubMed
概括
此摘要是机器生成的。

我们引入了一个新的间隔共识模型 (ICM) 来寻找未知的真理的共享知识,扩展文化共识理论 (CCT) 来从连续边界间隔响应中估计共识间隔.

关键词:
贝叶斯模型是贝叶斯模型.连续的有界响应.文化共识理论文化共识理论时间间隔的反应.

更多相关视频

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

16.0K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K

相关实验视频

Last Updated: Jan 12, 2026

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.8K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

16.0K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.4K

科学领域:

  • 社会科学 社会科学 社会科学
  • 统计 统计 统计 统计
  • 认知科学 认知科学

背景情况:

  • 文化共识理论 (CCT) 聚合了对未知的真理的共享知识.
  • 现有的CCT模型侧重于点真理 (二分法,多分法,连续).
  • 风险评估等领域需要在间隔上达成共识,而不仅仅是点.

研究的目的:

  • 引入间隔共识模型 (ICM) 作为CCT的扩展.
  • 能够从连续的边界间隔响应中估计共识间隔.
  • 解决基于间隔的共识现有的CCT模型的局限性.

主要方法:

  • 开发了一种新的贝叶斯层次模型模型方法.
  • 从间隔响应中估计的潜在共识间隔.
  • 利用模拟研究来评估模型性能.

主要成果:

  • 在ICM有效估计共识间隔.
  • 在模拟研究中,ICM的表现优于简单的平均值和中位数.
  • 将ICM应用于口头量化器判断的经验数据.

结论:

  • ICM是CCT的一个有价值的延伸,用于基于间隔的共识.
  • 这个模型增强了对需要间隔判断的领域的理解.
  • ICM提供了一个统计学上可靠的方法来汇总间隔数据.