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

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

9.0K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
9.0K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.1K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.1K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
8.3K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.5K
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...
3.5K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.9K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.9K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.3K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.3K

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 journal

Random Responding Detection in Two Alternative Forced Choice Tests: <i>l</i> <sub><i>z</i></sub> vs. Optimal Appropriateness Measurement.

Applied psychological measurement·2026
Same journal

General formulae for transforming Pearson's r to the scale of Cohen's d.

Applied psychological measurement·2026
Same journal

babebi: An R Package for Bayesian Estimation and Validation in Small-N Two-Rater Pre-Post Designs.

Applied psychological measurement·2026
Same journal

A Tool for Agreement and Alignment Analysis in Binary Rating Tasks: The R Package scindex.

Applied psychological measurement·2026
Same journal

The EM Algorithm and Its Variants in Cognitive Diagnostic Models: Comparing Their Propensity for Boundaries, Extremes, Convergence, and Suboptimal Solutions.

Applied psychological measurement·2026
Same journal

When Perceptions of Social Desirability Differ: Implications for the Multidimensional Nominal Response Model of Faking.

Applied psychological measurement·2026

相关实验视频

Updated: Sep 16, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

对子群的标准误差估计 非不变性估计

Paul A Jewsbury1

  • 1ETS Research Institute, ETS, Princeton, NJ, USA.

Applied psychological measurement
|July 8, 2025
PubMed
概括
此摘要是机器生成的。

新的方法通过将错误依赖关系考虑在内,准确地评估分数,将分群之间的公平性联系起来. 这样可以更好地检测标准化测试中的公平性问题.

关键词:
这是一个桥梁,桥梁.在等同化方面,它是相当的.链接链接链接链接模式 模式 模式 模式这是标准错误的标准错误.亚种群的不变性

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

918
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

相关实验视频

Last Updated: Sep 16, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
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

918
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

科学领域:

  • 心理测量 心理测量 心理测量
  • 教育测量教育的测量
  • 统计建模 统计建模

背景情况:

  • 评分链接对于在不同尺度或条件下比较评估分数至关重要.
  • 确保公平性要求连接功能在不同亚群体之间是不变的.
  • 由于复杂的错误依赖,现有的方法难以准确评估子群差异.

研究的目的:

  • 开发和验证新的统计方法来评估分数连接不变性跨子群体.
  • 为了解决目前忽视链接错误依赖性的方法中标准错误的高估问题.
  • 提高在教育和心理评估中检测公平违规行为的能力.

主要方法:

  • 开发统计模型,明确纳入链接错误依赖关系.
  • 模拟研究是为了在各种条件下评估拟议方法的准确性和性能.
  • 将新方法应用于现实世界的数据集以进行实际验证.

主要成果:

  • 拟议的方法提供了准确的标准误差估计,用于链接得分的分群差异.
  • 忽视或误解链接错误依赖导致标准错误的高估.
  • 新的方法显著增加了检测子群体间非不变的统计能力.

结论:

  • 准确的对链接错误依赖性的核算对于在分数链接中有效的公平性评估至关重要.
  • 开发的方法提供了一种更可靠的方法,以确保标准化测试中的公平性.
  • 改进的标准错误估计有助于更有效地检测公平性问题,支持公平的评估实践.